From c5d882aed65cb77c5b0f3d31cc8bcf648c092423 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Wed, 5 Mar 2025 12:40:52 +0000
Subject: [PATCH 01/43] Started adding external potential for river based on
 reading acceleration field

---
 examples/HumanMobility/humanMobility.yml |  1 +
 examples/HumanMobility/makeRiver.py      | 50 +++++++++++++++++++
 examples/HumanMobility/run.sh            |  7 +++
 src/abm/Geography/river/potential.h      | 63 +++++++++++++++++++++---
 4 files changed, 113 insertions(+), 8 deletions(-)
 create mode 100644 examples/HumanMobility/makeRiver.py

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index d97a5ee40..a415197c2 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -53,3 +53,4 @@ PhysicalConstants:
 RiverPotential:
   position:        [5010., 5090.]  # y-coordinates of the river (internal units)
   mass:     1e6  # "Mass" of the "river" (internal units)
+  parameter_file:  river.hdf5
diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
new file mode 100644
index 000000000..29d0f0894
--- /dev/null
+++ b/examples/HumanMobility/makeRiver.py
@@ -0,0 +1,50 @@
+import numpy as np
+import h5py as h5
+import argparse as ap
+from scipy.ndimage import gaussian_filter
+
+# Parse arguments
+parser = ap.ArgumentParser()
+parser.add_argument("-f", "--file", type=str, default="river.hdf5")
+args = parser.parse_args()
+
+# River parameters (matching humanMobility.yml)
+box_size = [10000., 10000.]  # meters
+river_y = [5010., 5090.]     # river banks y-coordinates
+river_width = river_y[1] - river_y[0]
+river_center = np.mean(river_y)
+
+# Grid parameters
+grid_size = [100, 100]  # number of cells
+dx = box_size[0] / grid_size[0]
+dy = box_size[1] / grid_size[1]
+
+# Create acceleration field
+ax = np.zeros(grid_size)
+ay = np.zeros(grid_size)
+
+# Generate grid coordinates
+y_coords = np.linspace(0, box_size[1], grid_size[1])
+
+# Set acceleration field
+for i in range(grid_size[1]):
+    y = y_coords[i]
+    # Inside river
+    if river_y[0] <= y <= river_y[1]:
+        ax[:, i] = 1.0  # Flow along x-direction
+    else:
+        # Decay outside river
+        dist = min(abs(y - river_y[0]), abs(y - river_y[1]))
+        decay = np.exp(-dist / river_width)
+        ax[:, i] = decay
+
+# Smooth the acceleration field
+ax = gaussian_filter(ax, sigma=2.0)
+ay = gaussian_filter(ay, sigma=2.0)
+
+# Save to HDF5 file
+with h5.File(args.file, 'w') as f:
+    f.create_dataset("AccelerationField/ax", data=ax)
+    f.create_dataset("AccelerationField/ay", data=ay)
+    f.create_dataset("Header/BoxSize", data=box_size)
+    f.create_dataset("Header/GridSize", data=grid_size)
\ No newline at end of file
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 5a1f7350e..6fb91d836 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -13,5 +13,12 @@ then
     python3 makeIC.py -f humans.hdf5  #make_hdf5.py
 fi
 
+# Generate acceleration field for river
+if [ ! -e data/river.hdf5 ]
+then
+    echo "Generating acceleration field for the river..."
+    python3 makeRiver.py -f data/river.hdf5
+fi
+
 # Run SWIFT
 swift -g --threads=4 -n 10000 humanMobility.yml
diff --git a/src/abm/Geography/river/potential.h b/src/abm/Geography/river/potential.h
index 5f58da387..e63de6015 100644
--- a/src/abm/Geography/river/potential.h
+++ b/src/abm/Geography/river/potential.h
@@ -75,16 +75,63 @@ __attribute__((always_inline)) INLINE static float external_gravity_timestep(
  * We change acceleration of humans if they go inside the river.
  *
  * @param time The current time.
- * @param potential The proerties of the external potential.
+ * @param potential The properties of the external potential.
  * @param phys_const The physical constants in internal units.
- * @param g Pointer to the g-particle data.
+ * @param g Pointer to the g-particle data (representing a human).
  */
 __attribute__((always_inline)) INLINE static void external_gravity_acceleration(
     double time, const struct external_potential* restrict potential,
     const struct phys_const* restrict phys_const, struct gpart* restrict g) {
   
-    float dy;
-    
+    float x, y;
+    float dx, dy;
+    int box_size[2], accel_size[2];
+    float* accel_x, accel_y;
+    int index_low[2], index_high[2];
+    float *rx, *ry;
+    float ax_ll, ay_ll, ax_lr, ay_lr, ax_ul, ay_ul, ax_ur, ay_ur;
+    float *ax, *ay;
+
+    const double box_size[2] = {e->s->dim[0], e->s->dim[1]};
+
+    read_acceleration_field(filename, accel_x, accel_y, box_size); // only once!
+
+    // within the 4 anchor points, where are we:
+    // x_rel = ((x / box_size_x) * accel_size[0]) % 1;
+    // y_rel = ((y / box_size_y) * accel_size[1]) % 1;
+  
+    // get the location of a human
+    x = g->x[0];
+    y = g->x[1];
+
+    // find the nearest indices of the location of a human
+    find_nearest_indices_2D(x, y, box_size, accel_size, index_low, index_high, rx, ry);
+
+    // get x- and y-acceleration at the 4 anchor points
+    ax_ll = accel_x[index_low[0] + index_low[1] * accel_size[0]];
+    ay_ll = accel_y[index_low[0] + index_low[1] * accel_size[0]];
+    ax_lr = accel_x[index_high[0] + index_low[1] * accel_size[0]];
+    ay_lr = accel_y[index_high[0] + index_low[1] * accel_size[0]];
+    ax_ul = accel_x[index_low[0] + index_high[1] * accel_size[0]];
+    ay_ul = accel_y[index_low[0] + index_high[1] * accel_size[0]];
+    ax_ur = accel_x[index_high[0] + index_high[1] * accel_size[0]];
+    ay_ur = accel_y[index_high[0] + index_high[1] * accel_size[0];
+
+    // interpolate acceleration for a human at the location
+    // (be careful of periodic boundary condition if applicable)
+    bilinear_interpolation(ax_ll, ay_ll,
+                           ax_lr, ay_lr,
+                           ax_ul, ay_ul,
+                           ax_ur, ay_ur,
+                           rx,    ry,
+                           ax,    ay);
+
+    // particle_i.velocity.x += particle_i_accel_x * dt
+    // particle_i.velocity.y += particle_i_accel_y * dt
+
+    dy = get_distance_to_river(g->x[0], g->x[1], potential->y[0], potential->y[1]);
+
+/*     
     if(g->x[1] < potential->y[0])
       dy = g->x[1] - potential->y[0];
     else if(g->x[1] > potential->y[1])
@@ -98,10 +145,10 @@ __attribute__((always_inline)) INLINE static void external_gravity_acceleration(
   const float rinv3 = rinv * rinv * rinv;
 
   // The acceleration must change when the human is in the river
-  // g->a_grav[0] += potential->mass * dx * rinv3;
-  g->a_grav[1] += potential->mass * dy * rinv3;
-  // g->a_grav[2] += potential->mass * dz * rinv3;
-
+  // g->a_grav[0] = ax + potential->mass * dx * rinv3;
+  g->a_grav[1] = ay + potential->mass * dy * rinv3;
+  // g->a_grav[2] = az + potential->mass * dz * rinv3;
+ */
   // gravity_add_comoving_potential(g, value); // value ?
 
 }

From ffa97fc240f27f44ccffe9c961165430d60964db Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 25 Mar 2025 11:51:05 +0000
Subject: [PATCH 02/43] Finally works with hydro and gravity (self and
 external) together

---
 examples/HumanMobility/humanMobility.yml      |  24 ++-
 examples/HumanMobility/makeIC.py              | 159 +++++----------
 examples/HumanMobility/makeRiver.py           |  63 ++++--
 .../HumanMobility/plot_velocity_parallel.py   |  39 +++-
 examples/HumanMobility/run.sh                 |  10 +-
 examples/HumanMobility/visualise.sh           |  13 +-
 install_swift.sh                              |   4 +
 src/abm/Geography/river/potential.h           | 192 ++++++++++++------
 src/abm/HumanMobility/abm_iact.h              |  27 +--
 src/abm/HumanMobility/abm_utils.h             | 120 +++++++++++
 src/engine.c                                  |  11 +-
 src/engine.h                                  |   3 +-
 swift.c                                       |   1 +
 13 files changed, 444 insertions(+), 222 deletions(-)
 create mode 100644 src/abm/HumanMobility/abm_utils.h

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index a415197c2..6c675a43f 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -40,7 +40,25 @@ InitialConditions:
 
 PhysicalConstants:
   G: 1.0  # Gravitational constant in internal units
-  
+
+# Add these gravity parameters
+Gravity:
+  MAC: adaptive           # Multipole Acceptance Criterion (adaptive/geometric)
+  mesh_side_length: 32     # Number of cells in each dimension of the gravity mesh
+  scale_factor_a: 1.0      # Scale factor for cosmological simulations (1.0 for non-cosmological)
+  theta: 0.7              # Opening angle for the gravity calculation
+  theta_cr: 0.5          # Critical opening angle for adaptive MAC
+  theta_min: 0.0         # Minimum opening angle
+  max_smoothing_iterations: 3  # Maximum number of iterations for force smoothing
+  epsilon_fmm: 0.001     # Accuracy parameter for FMM
+  eta: 0.025             # Constant controlling force accuracy in tree-PM algorithm
+  comoving_DM_softening: 0.0  # Softening length for gravity in internal units
+  max_physical_DM_softening: 0.0  # Max physical softening length
+  comoving_baryon_softening: 1.0  # Softening length for gas particles
+  max_physical_baryon_softening: 1.0  # Max physical softening for gas
+  mesh_uses_cell_grad: 1   # Whether to use cell gradients in mesh gravity
+  periodic: 1             # Use periodic gravity (matches InitialConditions)
+
 # ConstantPotential:
 #   g_cgs:      [0., -98, 0.]  # Earth acceleration along z-axis (cgs units)
 
@@ -51,6 +69,6 @@ PhysicalConstants:
 #   # timestep_mult:             0.1  # (Optional) The dimensionless constant C in the time-step condition
 
 RiverPotential:
-  position:        [5010., 5090.]  # y-coordinates of the river (internal units)
+  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
   mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  river.hdf5
+  parameter_file:  river.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/makeIC.py b/examples/HumanMobility/makeIC.py
index bbe9e1eae..55ab0fe29 100644
--- a/examples/HumanMobility/makeIC.py
+++ b/examples/HumanMobility/makeIC.py
@@ -82,21 +82,40 @@ def generate_ids(self):
 
         return self.ids
 
-    def save_to_gadget(self, filename, boxsize=10000):
+    def save_to_gadget(self, filename, boxsize=10000, type="particles"):
         """
         Save the human data to a GADGET .hdf5 file.
 
         Uses the internal options, but you must specify a filename.
+    
+        Parameters:
+            filename (str): The output HDF5 filename
+            boxsize (float): Size of the simulation box
+            type (str): Type of particles to write - either "gas" or "particles"
         """
+        # Validate particle type
+        if type not in ["gas", "particles"]:
+            raise ValueError('type must be either "gas" or "particles"')
+            
+        # Map particle type to index
+        type_index = 0 if type == "gas" else 1
+        
+        # Create arrays with the correct particle type index
+        np_total = np.zeros(6, dtype=int)
+        np_total[type_index] = self.nhumans
+        
+        mass_table = np.zeros(6)
+        mass_table[type_index] = self.humanmass
+
         with h5.File(filename, "w") as handle:
             wg.write_header(
                 handle,
                 boxsize=[boxsize, boxsize],
                 flag_entropy=0,
-                np_total=np.array([0, self.nhumans, 0, 0, 0, 0]),
+                np_total=np_total,
                 np_total_hw=np.array([0, 0, 0, 0, 0, 0]),
                 other={
-                    "MassTable": np.array([0, self.humanmass, 0, 0, 0, 0]),
+                    "MassTable": mass_table,
                     "Time": 0,
                     "Dimension": 2,
                     "Flag_Entropy_ICs": 0,
@@ -111,7 +130,7 @@ def save_to_gadget(self, filename, boxsize=10000):
 
             wg.write_block(
                 handle,
-                1,  # gas, # particles, dark matter
+                type_index,  # gas, dark matter particles
                 self.positions,
                 self.velocities,
                 self.ids,
@@ -129,19 +148,20 @@ def gen_humans_grid(meta):
     Generates humans on a grid and returns a filled Humans object.
     """
     humans = Humans(meta)
-    range = (0, meta["boxsize"])
-    centre_of_ring = [meta["boxsize"] / 2.0] * 3
+    positions = (0, meta["boxsize"]) # -0.5*meta["boxsize"]
+    # centre_of_ring = [meta["boxsize"] * 0.0] * 3
 
     # Because we are using a uniform grid we actually use the same x and y
-    # range for the initial human setup.
-    step = (range[1] - range[0]) / meta["nhumans"]
+    # positions for the initial human setup.
+    width = positions[1] - positions[0]
+    step = width / meta["nhumans"]
 
-    x_values = np.arange(0, range[1] - range[0], step)
+    x_values = np.arange(0, width, step, dtype=float) # -0.5*width
 
     # These are 2d arrays which isn't actually that helpful.
     x, y = np.meshgrid(x_values, x_values)
-    x = x.flatten() + centre_of_ring[0] - (range[1] - range[0]) / 2 + np.random.rand(humans.nhumans) * 0.001
-    y = y.flatten() + centre_of_ring[1] - (range[1] - range[0]) / 2 + np.random.rand(humans.nhumans) * 0.001
+    x = x.flatten() + np.random.rand(humans.nhumans) * 5
+    y = y.flatten() + np.random.rand(humans.nhumans) * 5
     z = np.zeros(humans.nhumans)
     # z = np.zeros_like(x) + meta["boxsize"] / 2
 
@@ -257,6 +277,20 @@ def gen_humans_grid(meta):
         default=10000,
     )
 
+    PARSER.add_argument(
+        "-t",
+        "--type",
+        help="""
+            Type of particles to use - either 'gas' or 'particles'.
+            'gas' will save humans as SPH particles (PartType0),
+            'particles' will save them as dark matter particles (PartType1).
+            Default: gas
+            """,
+        required=False,
+        choices=['gas', 'particles'],
+        default='gas',
+    )
+
     ### --- ### --- Argument Parsing --- ### --- ###
 
     ARGS = vars(PARSER.parse_args())
@@ -287,102 +321,11 @@ def gen_humans_grid(meta):
 
     HUMANS = gen_humans(META)
 
-    HUMANS.save_to_gadget(filename=ARGS["filename"], boxsize=ARGS["boxsize"])
-
+    # For SPH gas particles (PartType0) or dark matter particles (PartType1)
+    HUMANS.save_to_gadget(
+        filename=ARGS["filename"], 
+        boxsize=ARGS["boxsize"], 
+        type=ARGS["type"]
+    )
     print("Initial condition generated")
 
-
-#################################################
-
-# # Generates a SWIFT IC file with ...
-
-# # Parameters
-# periodic = 0  # 1 For periodic box
-# boxSize = 10  # 1 km
-# rho = 200  # Population density in code units ?
-# T = 1  # Initial intensity of human motion (how many people are moving in a box or percentage?)
-# gamma = 5.0 / 3.0  # Gas adiabatic index
-# fileName = "mobilityBox.hdf5"
-# # ---------------------------------------------------
-
-# # defines some constants
-# # need to be changed in plotTemperature.py too
-# h_frac = 0.76
-# mu = 4.0 / (1.0 + 3.0 * h_frac)
-
-# m_h_cgs = 1.67e-24
-# k_b_cgs = 1.38e-16
-
-# # defines units
-# unit_length = 1  # 1m
-# unit_mass = 1  # a unit mass of 1 particle-human
-# unit_time = 1  # 1 s ?
-
-# # Read id, position and h from glass
-# glass = h5.File("humans.hdf5", "r")
-# ids = glass["/PartType0/ParticleIDs"][:]
-# pos = glass["/PartType0/Coordinates"][:, :] * boxSize
-# h = glass["/PartType0/SmoothingLength"][:] * boxSize
-
-# # Compute basic properties
-
-# # need to define `pos`
-
-# numHum = np.size(pos) // 2
-# mass = boxSize ** 2 * rho # number of humans in a unit of territory
-# internalEnergy = k_b_cgs * T * mu / ((gamma - 1.0) * m_h_cgs)
-# internalEnergy *= (unit_time / unit_length) ** 2
-
-# # File
-# f = h5.File(fileName, "w")
-
-# # Header
-# grp = f.create_group("/Header")
-# grp.attrs["BoxSize"] = boxSize
-# grp.attrs["NumHum_Total"] = [numHum, 0, 0, 0, 0, 0]
-# grp.attrs["NumHum_Total_HighWord"] = [0, 0, 0, 0, 0, 0]
-# grp.attrs["NumHum_ThisFile"] = [numHum, 0, 0, 0, 0, 0]
-# grp.attrs["Time"] = 0.0
-# grp.attrs["NumFilesPerSnapshot"] = 1
-# grp.attrs["MassTable"] = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
-# # grp.attrs["Flag_Entropy_ICs"] = 0
-
-# # Runtime parameters
-# grp = f.create_group("/RuntimePars")
-# grp.attrs["PeriodicBoundariesOn"] = periodic
-
-# # Units
-# grp = f.create_group("/Units")
-# grp.attrs["Unit length in cgs (U_L)"] = unit_length
-# grp.attrs["Unit mass in cgs (U_M)"] = unit_mass
-# grp.attrs["Unit time in cgs (U_t)"] = unit_time
-# grp.attrs["Unit current in cgs (U_I)"] = 1.0
-# grp.attrs["Unit temperature in cgs (U_T)"] = 1.0
-
-# # Particle group
-# grp = f.create_group("/PartType0") # humans
-
-# v = np.zeros((numHum, 2))
-# ds = grp.create_dataset("Velocities", (numHum, 2), "f")
-# ds[()] = v
-
-# m = np.full((numHum, 1), mass)
-# ds = grp.create_dataset("Masses", (numHum, 1), "f")
-# ds[()] = m
-
-# h = np.reshape(h, (numHum, 1))
-# ds = grp.create_dataset("SmoothingLength", (numHum, 1), "f")
-# ds[()] = h
-
-# u = np.full((numHum, 1), internalEnergy)
-# ds = grp.create_dataset("InternalEnergy", (numHum, 1), "f")
-# ds[()] = u
-
-# ids = np.reshape(ids, (numHum, 1))
-# ds = grp.create_dataset("ParticleIDs", (numHum, 1), "L")
-# ds[()] = ids
-
-# ds = grp.create_dataset("Coordinates", (numHum, 2), "d")
-# ds[()] = pos
-
-# f.close()
diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index 29d0f0894..99f9a2d1f 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -13,38 +13,69 @@
 river_y = [5010., 5090.]     # river banks y-coordinates
 river_width = river_y[1] - river_y[0]
 river_center = np.mean(river_y)
+mass = 1e6                 # "mass" of the river
+distance = 1.0          # minimal distance from the river
 
 # Grid parameters
-grid_size = [100, 100]  # number of cells
-dx = box_size[0] / grid_size[0]
-dy = box_size[1] / grid_size[1]
+x_grid_size = 1000
+y_grid_size = 1000
+grid_size = [x_grid_size+1, y_grid_size+1]  # number of cells
 
 # Create acceleration field
-ax = np.zeros(grid_size)
-ay = np.zeros(grid_size)
+ax = np.zeros(grid_size, dtype=np.float32)
+ay = np.zeros(grid_size, dtype=np.float32)
 
 # Generate grid coordinates
 y_coords = np.linspace(0, box_size[1], grid_size[1])
 
 # Set acceleration field
+# ax component is zero outside river
+ax[:, :] = 0
 for i in range(grid_size[1]):
     y = y_coords[i]
-    # Inside river
-    if river_y[0] <= y <= river_y[1]:
-        ax[:, i] = 1.0  # Flow along x-direction
+
+### FOR THE RIVER FROM WEST TO EAST (simple example) ###
+    # Determine distance from river
+    if y < river_y[0]-distance:
+        dy = y - river_y[0]
+    elif y > river_y[1]+distance:
+        dy = y - river_y[1]
     else:
-        # Decay outside river
-        dist = min(abs(y - river_y[0]), abs(y - river_y[1]))
-        decay = np.exp(-dist / river_width)
-        ax[:, i] = decay
+        # Inside the river
+        dy = distance
+    
+    # Calculate acceleration using inverse cube law (rinv3)
+    r = np.sqrt(dy * dy)
+    rinv = 1.0 / r
+    rinv3 = rinv * rinv * rinv
+    
+    # Set acceleration components
+    # ay component follows inverse cube law
+    ay[:, i] = mass * dy * rinv3
 
 # Smooth the acceleration field
-ax = gaussian_filter(ax, sigma=2.0)
-ay = gaussian_filter(ay, sigma=2.0)
+#ax = gaussian_filter(ax, sigma=1.0)
+#ay = gaussian_filter(ay, sigma=1.0)
+### END RIVER ###
 
 # Save to HDF5 file
 with h5.File(args.file, 'w') as f:
+    print(f"Creating acceleration field with shape: {ax.shape}")
+    print(f"Grid size: {grid_size}")
+    print(f"Box size: {box_size}")
+    print(f"Mass: {mass}")
+    
     f.create_dataset("AccelerationField/ax", data=ax)
     f.create_dataset("AccelerationField/ay", data=ay)
-    f.create_dataset("Header/BoxSize", data=box_size)
-    f.create_dataset("Header/GridSize", data=grid_size)
\ No newline at end of file
+    f.create_group("Header")
+    f["Header"].attrs["BoxSize"] = np.array(box_size, dtype=np.float64)
+    f["Header"].attrs["GridSize"] = np.array(grid_size, dtype=np.int32)
+    f["Header"].attrs["Mass"] = np.array(mass, dtype=np.float64)
+    f["Header"].attrs["Distance"] = np.array(distance, dtype=np.float64)
+    
+    # Verify data was written correctly
+    print(f"Wrote ax with shape: {f['AccelerationField/ax'].shape}")
+    print(f"Wrote ay with shape: {f['AccelerationField/ay'].shape}")
+    print(f"Header/BoxSize: {f['Header'].attrs['BoxSize']}")
+    print(f"Header/GridSize: {f['Header'].attrs['GridSize']}")
+    print(f"Header/GridSize: {f['Header'].attrs['Mass']}")
\ No newline at end of file
diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index 4ef46ab0b..46db93f78 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -12,7 +12,12 @@
 import multiprocessing
 from functools import partial
 
-def process_file(i, min_x, max_x, min_y, max_y):
+def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
+
+    with h5py.File("river.hdf5", 'r') as sim:
+        mass = sim["/Header"].attrs["Mass"]
+
+    # Define the filename pattern for the HDF5 files and the PNG files
     filename_hdf5 = "data/humanMobility_%04d.hdf5" % i  # Adjust the filename pattern as needed
     filename_png = "images/humanMobility_%04d.png" % i
 
@@ -36,19 +41,23 @@ def process_file(i, min_x, max_x, min_y, max_y):
 
         # point_mass = sim["/Parameters"].attrs["PointMassPotential:mass"]
 
-        mass = sim["/Parameters"].attrs["RiverPotential:mass"]
-
         git = sim["Code"].attrs["Git Revision"]
 
-        pos = sim["/PartType1/Coordinates"][:, :]
+        # Use PartType0 for gas, PartType1 for particles
+        part_type = "PartType0" if _type == "gas" else "PartType1"
+        
+        pos = sim[f"/{part_type}/Coordinates"][:, :]
         x = pos[:, 0] - boxSize / 2
         y = pos[:, 1] - boxSize / 2
-        vel = sim["/PartType1/Velocities"][:, :]
+        vel = sim[f"/{part_type}/Velocities"][:, :]
         v_norm = np.sqrt(vel[:, 0] ** 2 + vel[:, 1] ** 2)
-        # rho = sim["/PartType0/Densities"][:]
-        # u = sim["/PartType0/InternalEnergies"][:]
-        # S = sim["/PartType0/Entropies"][:]
-        # P = sim["/PartType0/Pressures"][:]
+        # mass = sim["/UnusedParameters"].attrs["RiverPotential:mass"]
+        if _type == "gas":
+          rho = sim["/PartType0/Densities"][:]
+          u = sim["/PartType0/InternalEnergies"][:]
+          S = sim["/PartType0/Entropies"][:]
+          P = sim["/PartType0/Pressures"][:]
+
 
         X = pos[:, 0]
         Y = pos[:, 1]
@@ -71,6 +80,7 @@ def process_file(i, min_x, max_x, min_y, max_y):
         scale = max_M / desired_max_arrow_length
 
         # Plotting the velocity vectors using quiver
+
         # Create the plot
         fig, ax = plt.subplots()
         ax.set_title("Velocity map of human mobility %04d (scale=%07.2d)\n \"river mass\"=%s" % (i, scale, mass))
@@ -127,9 +137,18 @@ def process_file(i, min_x, max_x, min_y, max_y):
     max_x = int(sys.argv[3])
     min_y = int(sys.argv[4])
     max_y = int(sys.argv[5])
+    _type = sys.argv[6]
+
+    if _type not in ["gas", "particles"]:
+        raise ValueError("type must be either 'gas' or 'particles'")
 
     # Create a partial function with fixed additional arguments
-    partial_process_file = partial(process_file, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
+    partial_process_file = partial(process_file,
+                                   min_x=min_x,
+                                   max_x=max_x,
+                                   min_y=min_y,
+                                   max_y=max_y,
+                                   _type=_type)
 
     file_indices = list(range(num_files))
 
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 6fb91d836..d2d7aa707 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -3,6 +3,7 @@
 
 # Remove previously generated data and images to regenerate the new ones
 rm humans.hdf5
+rm river.hdf5
 rm data/*
 #rm images/*
 
@@ -10,15 +11,16 @@ rm data/*
 if [ ! -e humans.hdf5 ]
 then
     echo "Generating initial conditions for the human mobility box example..."
-    python3 makeIC.py -f humans.hdf5  #make_hdf5.py
+    python3 makeIC.py -t gas -f humans.hdf5  # -t particles
 fi
 
 # Generate acceleration field for river
-if [ ! -e data/river.hdf5 ]
+if [ ! -e river.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRiver.py -f data/river.hdf5
+    python3 makeRiver.py -f river.hdf5
 fi
 
 # Run SWIFT
-swift -g --threads=4 -n 10000 humanMobility.yml
+# gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
+swift -A -s -g -G --threads=4 -n 10000 humanMobility.yml #
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index 7a72a1747..f73baeb07 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -2,14 +2,15 @@
 
 rm images/*
 
-num_files=501
-min_x=3000
-max_x=7000
-min_y=3000
-max_y=7000
+num_files=407 #501
+min_x=0
+max_x=10000
+min_y=0
+max_y=10000
+type="gas"  # or "particles"
 
 # Plot the result
-python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y}
+python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type}
 python3 generate_GIF.py ${num_files}
 
 # This command sets:
diff --git a/install_swift.sh b/install_swift.sh
index 460a58022..dc674ed3c 100755
--- a/install_swift.sh
+++ b/install_swift.sh
@@ -15,6 +15,7 @@ make clean
 echo "#############################"
 echo "# Running './configure' ... #"
 echo "#############################"
+export CFLAGS="-fsanitize=address -g"
 ./configure CC=gcc \
 	    LDFLAGS=-L${LOCAL_LIBRARY_PATH} \
 	    --enable-mpi \
@@ -27,6 +28,9 @@ echo "#############################"
 		--with-ext-potential=human-mobility \
 	    --with-hm=river
 
+	    # --with-hm=random-walk
+
+
 echo "########################################"
 echo "# Running 'make -j\$(nproc --all)' ... #"
 echo "########################################"
diff --git a/src/abm/Geography/river/potential.h b/src/abm/Geography/river/potential.h
index e63de6015..337ea5112 100644
--- a/src/abm/Geography/river/potential.h
+++ b/src/abm/Geography/river/potential.h
@@ -32,6 +32,8 @@
 #include "physical_constants.h"
 #include "space.h"
 #include "units.h"
+#include "abm/HumanMobility/abm_utils.h"
+#include "common_io.h"
 
 /**
  * @brief External Potential Properties - River
@@ -45,11 +47,11 @@ struct external_potential {
   /*! Mass */
   double mass;
 
-  /*! Width */
-  // double width;
-
-  /*! Time-step condition pre-factor */
-  // float timestep_mult;
+  /*! Cached acceleration field data */
+  float *ax;
+  float *ay;
+  double box_size[2];
+  int grid_size[2];
 };
 
 
@@ -75,7 +77,7 @@ __attribute__((always_inline)) INLINE static float external_gravity_timestep(
  * We change acceleration of humans if they go inside the river.
  *
  * @param time The current time.
- * @param potential The properties of the external potential.
+ * @param potential The properties of the external potential (representing a river).
  * @param phys_const The physical constants in internal units.
  * @param g Pointer to the g-particle data (representing a human).
  */
@@ -83,39 +85,43 @@ __attribute__((always_inline)) INLINE static void external_gravity_acceleration(
     double time, const struct external_potential* restrict potential,
     const struct phys_const* restrict phys_const, struct gpart* restrict g) {
   
-    float x, y;
-    float dx, dy;
-    int box_size[2], accel_size[2];
-    float* accel_x, accel_y;
-    int index_low[2], index_high[2];
-    float *rx, *ry;
+    double x, y;
+    double box_size[2];
+    int accel_size[2];
+    float *accel_x, *accel_y;
+    int xi, yi;
+    float rx, ry;
     float ax_ll, ay_ll, ax_lr, ay_lr, ax_ul, ay_ul, ax_ur, ay_ur;
-    float *ax, *ay;
-
-    const double box_size[2] = {e->s->dim[0], e->s->dim[1]};
-
-    read_acceleration_field(filename, accel_x, accel_y, box_size); // only once!
+    float ax, ay;
 
-    // within the 4 anchor points, where are we:
-    // x_rel = ((x / box_size_x) * accel_size[0]) % 1;
-    // y_rel = ((y / box_size_y) * accel_size[1]) % 1;
-  
     // get the location of a human
     x = g->x[0];
     y = g->x[1];
+    box_size[0] = potential->box_size[0];
+    box_size[1] = potential->box_size[1];
+    accel_size[0] = potential->grid_size[0];
+    accel_size[1] = potential->grid_size[1];
+    accel_x = potential->ax;
+    accel_y = potential->ay;
 
     // find the nearest indices of the location of a human
-    find_nearest_indices_2D(x, y, box_size, accel_size, index_low, index_high, rx, ry);
+    find_nearest_indices_2D(x, y, box_size, accel_size, &xi, &yi, &rx, &ry); //+0.5*box_size[0]
+
+    // correct the indices if they are out of the range
+    if (xi < 0) xi = 0;
+    if (xi >= accel_size[0]-1) xi = accel_size[0]-2;
+    if (yi < 0) yi = 0;
+    if (yi >= accel_size[1]-1) yi = accel_size[1]-2;
 
     // get x- and y-acceleration at the 4 anchor points
-    ax_ll = accel_x[index_low[0] + index_low[1] * accel_size[0]];
-    ay_ll = accel_y[index_low[0] + index_low[1] * accel_size[0]];
-    ax_lr = accel_x[index_high[0] + index_low[1] * accel_size[0]];
-    ay_lr = accel_y[index_high[0] + index_low[1] * accel_size[0]];
-    ax_ul = accel_x[index_low[0] + index_high[1] * accel_size[0]];
-    ay_ul = accel_y[index_low[0] + index_high[1] * accel_size[0]];
-    ax_ur = accel_x[index_high[0] + index_high[1] * accel_size[0]];
-    ay_ur = accel_y[index_high[0] + index_high[1] * accel_size[0];
+    ax_ll = accel_x[xi * accel_size[0] + yi];
+    ay_ll = accel_y[xi * accel_size[0] + yi];
+    ax_lr = accel_x[(xi+1) * accel_size[0] + yi];
+    ay_lr = accel_y[(xi+1) * accel_size[0] + yi];
+    ax_ul = accel_x[xi * accel_size[0] + (yi+1)];
+    ay_ul = accel_y[xi * accel_size[0] + (yi+1)];
+    ax_ur = accel_x[(xi+1) * accel_size[0] + (yi+1)];
+    ay_ur = accel_y[(xi+1) * accel_size[0] + (yi+1)];
 
     // interpolate acceleration for a human at the location
     // (be careful of periodic boundary condition if applicable)
@@ -124,33 +130,16 @@ __attribute__((always_inline)) INLINE static void external_gravity_acceleration(
                            ax_ul, ay_ul,
                            ax_ur, ay_ur,
                            rx,    ry,
-                           ax,    ay);
-
-    // particle_i.velocity.x += particle_i_accel_x * dt
-    // particle_i.velocity.y += particle_i_accel_y * dt
-
-    dy = get_distance_to_river(g->x[0], g->x[1], potential->y[0], potential->y[1]);
-
-/*     
-    if(g->x[1] < potential->y[0])
-      dy = g->x[1] - potential->y[0];
-    else if(g->x[1] > potential->y[1])
-      dy = g->x[1] - potential->y[1];
-    else {
-      dy = 0;
-      message("Human is inside the river!!!");
-    }
-
-  const float rinv = 1.f / sqrtf(dy * dy);
-  const float rinv3 = rinv * rinv * rinv;
-
-  // The acceleration must change when the human is in the river
-  // g->a_grav[0] = ax + potential->mass * dx * rinv3;
-  g->a_grav[1] = ay + potential->mass * dy * rinv3;
-  // g->a_grav[2] = az + potential->mass * dz * rinv3;
- */
-  // gravity_add_comoving_potential(g, value); // value ?
+                           &ax,   &ay);
+
+  // if (ax > 1e+10 || ay > 1e+10)
+  //   error("Acceleration is too high (human is inside river): %f %f", ax, ay);
+
+  g->a_grav[0] += ax;
+  g->a_grav[1] += ay;
+  // g->a_grav[2] = az;
 
+  // gravity_add_comoving_potential(g, value); // value ?
 }
 
 /**
@@ -171,6 +160,66 @@ external_gravity_get_potential_energy(
   return 0.f;
 }
 
+/**
+ * 
+*/
+static INLINE void geography_read_acceleration_field(
+    struct swift_params* parameter_file, struct external_potential* potential) {
+#if defined(HAVE_HDF5)
+
+  /*! Acceleration field filename */
+  char filename[DESCRIPTION_BUFFER_SIZE];
+  
+  /* Read acceleration field file path */
+  parser_get_param_string(parameter_file, "RiverPotential:parameter_file", filename);
+
+  /* Load acceleration field data */
+
+  /* Open file */
+  hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);
+  if (file_id < 0) error("Unable to open file %s", filename);
+
+  /* Read grid size and box size */
+
+  /* Open group */
+  hid_t group_id = H5Gopen(file_id, "Header", H5P_DEFAULT);
+  if (group_id < 0) error("unable to open group Header.\n");
+
+  /* Read the arrays */
+  io_read_array_attribute(group_id, "BoxSize", DOUBLE, potential->box_size, 2);
+  io_read_array_attribute(group_id, "GridSize", INT, potential->grid_size, 2);
+
+  /* Close group */
+  hid_t status = H5Gclose(group_id);
+  if (status < 0) error("error closing group.");
+
+  /* Allocate and read acceleration fields */
+  const int size = potential->grid_size[0] * potential->grid_size[1];
+  potential->ax = (float*)malloc(size * sizeof(float));
+  potential->ay = (float*)malloc(size * sizeof(float));
+  printf("size: %d\n", size);
+
+  /* Open group */
+  group_id = H5Gopen(file_id, "AccelerationField", H5P_DEFAULT);
+  if (group_id < 0) error("unable to open group AccelerationField.\n");
+
+  /* Read the datasets */
+  io_read_array_dataset(group_id, "ax", FLOAT, potential->ax, size);
+  io_read_array_dataset(group_id, "ay", FLOAT, potential->ay, size);
+
+  /* Close group */
+  status = H5Gclose(group_id);
+  if (status < 0) error("error closing group.");
+
+  /* Close file */
+  status = H5Fclose(file_id);
+  if (status < 0) error("error closing file.");
+
+#else
+  message("Cannot read the acceleration field without HDF5");
+#endif
+}
+
 /**
  * @brief Initialises the external potential properties in the internal system
  * of units.
@@ -187,14 +236,17 @@ static INLINE void potential_init_backend(
     const struct unit_system* us, const struct space* s,
     struct external_potential* potential) {
 
-  /* Read in the position of the centre of potential */
-  parser_get_param_double_array(parameter_file, "RiverPotential:position", // northern and southern bank coordinatates
+  /* Read river banks position */
+  parser_get_param_double_array(parameter_file,
+                                "RiverPotential:position", // northern and southern bank coordinatates
                                 2, potential->y);
 
-  /* Read the other parameters of the model */
+  /* Read mass parameter */
   potential->mass =
       parser_get_param_double(parameter_file, "RiverPotential:mass");
 
+  /* Load acceleration field data */
+  geography_read_acceleration_field(parameter_file, potential);
 }
 
 /**
@@ -208,4 +260,26 @@ static INLINE void potential_print_backend(
   message("External potential is 'River'.");
 }
 
+/**
+ * @brief Cleans up the external potential by freeing allocated memory.
+ *
+ * @param potential The external potential to clean up.
+ */
+static INLINE void potential_cleanup_backend(
+  struct external_potential* potential) {
+
+#if defined(HAVE_HDF5)
+/* Free acceleration field arrays if they were allocated */
+if (potential->ax != NULL) {
+  free(potential->ax);
+  potential->ax = NULL;
+}
+
+if (potential->ay != NULL) {
+  free(potential->ay);
+  potential->ay = NULL;
+}
+#endif
+
+}
 #endif /* SWIFT_POTENTIAL_RIVER_H */
diff --git a/src/abm/HumanMobility/abm_iact.h b/src/abm/HumanMobility/abm_iact.h
index 69d758d50..1df018ff0 100644
--- a/src/abm/HumanMobility/abm_iact.h
+++ b/src/abm/HumanMobility/abm_iact.h
@@ -324,6 +324,12 @@ __attribute__((always_inline)) INLINE static void runner_iact_force(
   const float visc_acc_term =
       0.5f * visc * (wi_dr * f_ij + wj_dr * f_ji) * r_inv;
 
+#ifdef HM_CASE_RANDOMWALK
+  // Seed the random number generator with the current time
+  srand(time(NULL));
+  _kick_random_walk(pi, dx, abm_max_rand, abm_div_rand);
+  _kick_random_walk(pj, dx, abm_max_rand, abm_div_rand);
+#else
   /* SPH acceleration term */
   const float sph_acc_term =
       (P_over_rho2_i * wi_dr + P_over_rho2_j * wj_dr) * r_inv;
@@ -335,12 +341,6 @@ __attribute__((always_inline)) INLINE static void runner_iact_force(
   /* Assemble the acceleration */
   const float acc = sph_acc_term + visc_acc_term + adapt_soft_acc_term;
 
-#ifdef HM_CASE_RANDOMWALK
-  // Seed the random number generator with the current time
-  srand(time(NULL));
-  _kick_random_walk(pi, dx, abm_max_rand, abm_div_rand);
-  _kick_random_walk(pj, dx, abm_max_rand, abm_div_rand);
-#else
   /* Use the force Luke ! */
   pi->a_hydro[0] -= mj * acc * dx[0];
   pi->a_hydro[1] -= mj * acc * dx[1];
@@ -413,7 +413,6 @@ __attribute__((always_inline)) INLINE static void runner_iact_nonsym_force(
   const float rhoi = pi->rho;
   const float rhoj = pj->rho;
   const float pressurei = pi->force.pressure;
-  const float pressurej = pj->force.pressure;
 
   /* Get the kernel for hi. */
   const float hi_inv = 1.0f / hi;
@@ -437,7 +436,6 @@ __attribute__((always_inline)) INLINE static void runner_iact_nonsym_force(
 
   /* Compute gradient terms */
   const float P_over_rho2_i = pressurei / (rhoi * rhoi) * f_ij;
-  const float P_over_rho2_j = pressurej / (rhoj * rhoj) * f_ji;
 
   /* Compute dv dot r. */
   const float dvdr = (pi->v[0] - pj->v[0]) * dx[0] +
@@ -466,6 +464,14 @@ __attribute__((always_inline)) INLINE static void runner_iact_nonsym_force(
   const float visc_acc_term =
       0.5f * visc * (wi_dr * f_ij + wj_dr * f_ji) * r_inv;
 
+#ifdef HM_CASE_RANDOMWALK
+  // Seed the random number generator with the current time
+  srand(time(NULL));
+  _kick_random_walk(pi, dx, abm_max_rand, abm_div_rand);
+#else
+  const float pressurej = pj->force.pressure;
+  const float P_over_rho2_j = pressurej / (rhoj * rhoj) * f_ji;
+
   /* SPH acceleration term */
   const float sph_acc_term =
       (P_over_rho2_i * wi_dr + P_over_rho2_j * wj_dr) * r_inv;
@@ -477,11 +483,6 @@ __attribute__((always_inline)) INLINE static void runner_iact_nonsym_force(
   /* Assemble the acceleration */
   const float acc = sph_acc_term + visc_acc_term + adapt_soft_acc_term;
 
-#ifdef HM_CASE_RANDOMWALK
-  // Seed the random number generator with the current time
-  srand(time(NULL));
-  _kick_random_walk(pi, dx, abm_max_rand, abm_div_rand);
-#else
   /* Use the force Luke ! */
   pi->a_hydro[0] -= mj * acc * dx[0];
   pi->a_hydro[1] -= mj * acc * dx[1];
diff --git a/src/abm/HumanMobility/abm_utils.h b/src/abm/HumanMobility/abm_utils.h
new file mode 100644
index 000000000..95a4680a4
--- /dev/null
+++ b/src/abm/HumanMobility/abm_utils.h
@@ -0,0 +1,120 @@
+/*******************************************************************************
+ * This file is part of SWIFT_ABM.
+ * Copyright (c) 2025 Dmitry Nikolaenko (dmitry.nikolaenko@durham.ac.uk)
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as published
+ * by the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public License
+ * along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ *
+ ******************************************************************************/
+#ifndef SWIFT_HUMANMOBILITY_ABM_UTILS_H
+#define SWIFT_HUMANMOBILITY_ABM_UTILS_H
+
+/* Standard headers */
+#include <math.h>
+
+/**
+ * @brief A utility to set some initialisation parametres.
+ *
+ * @param h The human whose parameters are to be set.
+ */
+// __attribute__((always_inline)) INLINE static void _abm_rand_params(
+//     struct part *restrict h, const float max_rand, const float div_rand) {
+
+//   h->max_rand = max_rand;
+//   h->div_rand = div_rand;
+//   h->sub_rand = max_rand / div_rand / 2;
+// }
+
+
+/**
+ * @brief Search for indices of the nearest grid points for a given position (x, y)
+ *      in the 2D box of `accel_size`
+ *      describing the area of size `box_size` in which the human is located
+ *
+ * @param x The x-coordinate of the human location (x, y).
+ * @param y The y-coordinate of the human location (x, y).
+ * @param box_size The size of the box in which the human is located.
+ * @param accel_size The size of the acceleration grid for the box.
+ * @param xi The lower x-index of the human location (x, y) in the grid.
+ * @param yi The lower y-index of the human location (x, y) in the grid.
+ * @param rx The relative anchor `x` of the human location (x, y) in the grid.
+ * @param ry The relative anchor `y` of the human location (x, y) in the grid.
+ */
+__attribute__((always_inline))
+INLINE static void find_nearest_indices_2D(double x, double y,
+                                          const double* box_size,
+                                          const int* accel_size,
+                                          int* xi, int* yi,
+                                          float *rx, float *ry) {
+  *xi = (int)floor(x / box_size[0] * (accel_size[0]-1));
+  *yi = (int)floor(y / box_size[1] * (accel_size[1]-1));
+  *rx = fmod((x / box_size[0]) * (accel_size[0]-1), 1.0f);
+  *ry = fmod((y / box_size[1]) * (accel_size[1]-1), 1.0f);
+}
+
+
+
+/**
+ * @brief Bilinear interpolation of the acceleration grid for a given position (x, y)
+ *
+ * @param h The human whose parameters are to be set.
+ */
+__attribute__((always_inline)) INLINE static void bilinear_interpolation(
+    const float ax_ll, const float ay_ll,   // Lower-left accelerations
+    const float ax_lr, const float ay_lr,   // Lower-right accelerations  
+    const float ax_ul, const float ay_ul,   // Upper-left accelerations
+    const float ax_ur, const float ay_ur,   // Upper-right accelerations
+    const float rx, const float ry,         // Relative position (0-1)
+    float* ax_out, float* ay_out) {         // Output accelerations
+
+    /* Weights for the four corners */
+    const float w1 = (1.0f - rx) * (1.0f - ry);  // Lower-left
+    const float w2 = rx * (1.0f - ry);           // Lower-right
+    const float w3 = (1.0f - rx) * ry;           // Upper-left  
+    const float w4 = rx * ry;                    // Upper-right
+
+    /* Interpolate x-acceleration */
+    *ax_out = ax_ll * w1 + ax_lr * w2 + ax_ul * w3 + ax_ur * w4;
+
+    /* Interpolate y-acceleration */
+    *ay_out = ay_ll * w1 + ay_lr * w2 + ay_ul * w3 + ay_ur * w4;
+}
+
+
+/**
+ * @brief Check if a human is in the river (between the banks for a given position (x, y)).
+ *
+ * @param 
+ */
+__attribute__((always_inline)) INLINE static void check_human_in_river( ) {
+
+}
+
+/**
+ * @brief A utility to advance random walk of a human.
+ *
+ * @param h The human.
+ */
+__attribute__((always_inline)) INLINE static void _kick_random_walk(
+    struct part *restrict h, const float dx[3], const float max_rand, const float div_rand) {
+
+  const float sub_rand = max_rand / div_rand / 2;
+
+  /* Change a_hydro (only 2D for humans) */
+  h->a_hydro[0] = (fmod((float)rand(), max_rand) / div_rand - sub_rand) * dx[0];
+  h->a_hydro[1] = (fmod((float)rand(), max_rand) / div_rand - sub_rand) * dx[1];
+  h->a_hydro[2] = 0.0; //((rand()%max_rand)/div_rand - sub_rand) * dx[2];
+}
+
+
+#endif /* SWIFT_HUMANMOBILITY_ABM_UTILS_H */
diff --git a/src/engine.c b/src/engine.c
index 7a335c957..a4780ed65 100644
--- a/src/engine.c
+++ b/src/engine.c
@@ -134,7 +134,8 @@ const char *engine_policy_names[] = {"none",
                                      "line of sight",
                                      "sink",
                                      "rt",
-                                     "power spectra"};
+                                     "power spectra",
+                                     "abm"};
 
 const int engine_default_snapshot_subsample[swift_type_count] = {0};
 
@@ -3710,7 +3711,13 @@ void engine_recompute_displacement_constraint(struct engine *e) {
  * @param restart Was this a run that was restarted from check-point files?
  */
 void engine_clean(struct engine *e, const int fof, const int restart) {
-  /* Start by telling the runners to stop. */
+#ifdef EXTERNAL_POTENTIAL_ABM
+  printf("Cleaning up engine...\n");
+  // const int with_abm = e->policy & engine_policy_abm;
+  printf("Cleaning up the external potential\n");
+  potential_cleanup_backend((struct external_potential*)e->external_potential);
+#endif
+/* Start by telling the runners to stop. */
   e->step_props = engine_step_prop_done;
   swift_barrier_wait(&e->run_barrier);
 
diff --git a/src/engine.h b/src/engine.h
index a9ebcd22e..587dfcaea 100644
--- a/src/engine.h
+++ b/src/engine.h
@@ -89,8 +89,9 @@ enum engine_policy {
   engine_policy_sinks = (1 << 25),
   engine_policy_rt = (1 << 26),
   engine_policy_power_spectra = (1 << 27),
+  engine_policy_abm = (1 << 28),
 };
-#define engine_maxpolicy 28
+#define engine_maxpolicy 29
 extern const char *engine_policy_names[engine_maxpolicy + 1];
 
 /**
diff --git a/swift.c b/swift.c
index eca50ab20..8a535d664 100644
--- a/swift.c
+++ b/swift.c
@@ -1547,6 +1547,7 @@ int main(int argc, char *argv[]) {
     if (with_sinks) engine_policies |= engine_policy_sinks;
     if (with_rt) engine_policies |= engine_policy_rt;
     if (with_power) engine_policies |= engine_policy_power_spectra;
+    if (with_abm) engine_policies |= engine_policy_abm;
 
     /* Initialize the engine with the space and policies. */
     engine_init(&e, &s, params, output_options, N_total[swift_type_gas],

From 1eed01c25617459c8db15b90bfd08fd61bc8276c Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 25 Mar 2025 13:09:10 +0000
Subject: [PATCH 03/43] Forgotten new file

---
 src/abm/HumanMobility/abm_part.h | 235 +++++++++++++++++++++++++++++++
 1 file changed, 235 insertions(+)
 create mode 100644 src/abm/HumanMobility/abm_part.h

diff --git a/src/abm/HumanMobility/abm_part.h b/src/abm/HumanMobility/abm_part.h
new file mode 100644
index 000000000..4e33764f9
--- /dev/null
+++ b/src/abm/HumanMobility/abm_part.h
@@ -0,0 +1,235 @@
+/*******************************************************************************
+ * This file is part of SWIFT_ABM.
+ * Copyright (c) 2025 Dmitry Nikolaenko (dmitry.nikolaenko@durham.ac.uk)
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as published
+ * by the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public License
+ * along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ *
+ ******************************************************************************/
+#ifndef SWIFT_HUMANMOBILITY_ABM_PART_H
+#define SWIFT_HUMANMOBILITY_ABM_PART_H
+
+/**
+ * @file HumanMobility/abm_part.h
+ * @brief Minimal conservative implementation of SPH (Particle definition)
+ *
+ * The thermal variable is the internal energy (u). Simple constant
+ * viscosity term with the Balsara (1995) switch. No thermal conduction
+ * term is implemented.
+ *
+ * This corresponds to equations (43), (44), (45), (101), (103)  and (104) with
+ * \f$\beta=3\f$ and \f$\alpha_u=0\f$ of Price, D., Journal of Computational
+ * Physics, 2012, Volume 231, Issue 3, pp. 759-794.
+ */
+
+#include "adaptive_softening_struct.h"
+#include "black_holes_struct.h"
+#include "chemistry_struct.h"
+#include "cooling_struct.h"
+#include "feedback_struct.h"
+#include "mhd_struct.h"
+#include "particle_splitting_struct.h"
+#include "rt_struct.h"
+#include "sink_struct.h"
+#include "star_formation_struct.h"
+#include "timestep_limiter_struct.h"
+#include "tracers_struct.h"
+
+/**
+ * @brief Particle fields not needed during the SPH loops over neighbours.
+ *
+ * This structure contains the particle fields that are not used in the
+ * density or force loops. Quantities should be used in the kick, drift and
+ * potentially ghost tasks only.
+ */
+struct xpart {
+
+  /*! Offset between current position and position at last tree rebuild. */
+  float x_diff[3];
+
+  /*! Offset between the current position and position at the last sort. */
+  float x_diff_sort[3];
+
+  /*! Velocity at the last full step. */
+  float v_full[3];
+
+  /*! Gravitational acceleration at the end of the last step */
+  float a_grav[3];
+
+  /*! Internal energy at the last full step. */
+  float u_full;
+
+  /*! Additional data used to record particle splits */
+  struct particle_splitting_data split_data;
+
+  /*! Additional data used to record cooling information */
+  struct cooling_xpart_data cooling_data;
+
+  /* Additional data used by the tracers */
+  struct tracers_xpart_data tracers_data;
+
+  /* Additional data used by the tracers */
+  struct star_formation_xpart_data sf_data;
+
+  /*! Additional data used by the feedback */
+  struct feedback_xpart_data feedback_data;
+
+  /*! Additional data used by the MHD scheme */
+  struct mhd_xpart_data mhd_data;
+
+} SWIFT_STRUCT_ALIGN;
+
+/**
+ * @brief Particle fields for the SPH particles
+ *
+ * The density and force substructures are used to contain variables only used
+ * within the density and force loops over neighbours. All more permanent
+ * variables should be declared in the main part of the part structure,
+ */
+struct part {
+
+  /*! Particle unique ID. */
+  long long id;
+
+  /*! Pointer to corresponding gravity part. */
+  struct gpart* gpart;
+
+  /*! Particle position. */
+  double x[3];
+
+  /*! Particle predicted velocity. */
+  float v[3];
+
+  /*! Particle acceleration. */
+  float a_hydro[3];
+
+  /*! Particle mass. */
+  float mass;
+
+  /*! Particle smoothing length. */
+  float h;
+
+  /*! Particle internal energy. */
+  float u;
+
+  /*! Time derivative of the internal energy. */
+  float u_dt;
+
+  /*! Particle density. */
+  float rho;
+
+  /* Store density/force specific stuff. */
+  union {
+
+    /**
+     * @brief Structure for the variables only used in the density loop over
+     * neighbours.
+     *
+     * Quantities in this sub-structure should only be accessed in the density
+     * loop over neighbours and the ghost task.
+     */
+    struct {
+
+      /*! Neighbour number count. */
+      float wcount;
+
+      /*! Derivative of the neighbour number with respect to h. */
+      float wcount_dh;
+
+      /*! Derivative of density with respect to h */
+      float rho_dh;
+
+      /*! Velocity divergence */
+      float div_v;
+
+      /*! Velocity curl */
+      float rot_v[3];
+
+    } density;
+
+    /**
+     * @brief Structure for the variables only used in the force loop over
+     * neighbours.
+     *
+     * Quantities in this sub-structure should only be accessed in the force
+     * loop over neighbours and the ghost, drift and kick tasks.
+     */
+    struct {
+
+      /*! "Grad h" term */
+      float f;
+
+      /*! Particle pressure. */
+      float pressure;
+
+      /*! Particle soundspeed. */
+      float soundspeed;
+
+      /*! Particle signal velocity */
+      float v_sig;
+
+      /*! Time derivative of smoothing length  */
+      float h_dt;
+
+      /*! Balsara switch */
+      float balsara;
+
+    } force;
+  };
+
+  /*! Additional data used for adaptive softening */
+  struct adaptive_softening_part_data adaptive_softening_data;
+
+  /*! Additional data used by the MHD scheme */
+  struct mhd_part_data mhd_data;
+
+  /*! Chemistry information */
+  struct chemistry_part_data chemistry_data;
+
+  /*! Cooling information */
+  struct cooling_part_data cooling_data;
+
+  /*! Additional data used by the feedback */
+  struct feedback_part_data feedback_data;
+
+  /*! Black holes information (e.g. swallowing ID) */
+  struct black_holes_part_data black_holes_data;
+
+  /*! Sink information (e.g. swallowing ID) */
+  struct sink_part_data sink_data;
+
+  /*! Additional Radiative Transfer Data */
+  struct rt_part_data rt_data;
+
+  /*! RT sub-cycling time stepping data */
+  struct rt_timestepping_data rt_time_data;
+
+  /*! Time-step length */
+  timebin_t time_bin;
+
+  /*! Time-step limiter information */
+  struct timestep_limiter_data limiter_data;
+
+#ifdef SWIFT_DEBUG_CHECKS
+
+  /* Time of the last drift */
+  integertime_t ti_drift;
+
+  /* Time of the last kick */
+  integertime_t ti_kick;
+
+#endif
+
+} SWIFT_STRUCT_ALIGN;
+
+#endif /* SWIFT_HUMANMOBILITY_ABM_PART_H */

From 8a5a7470fb46a4a8f818b3ee29cde93eea11d7f1 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 25 Mar 2025 13:16:47 +0000
Subject: [PATCH 04/43] More forgotten files

---
 src/abm/abm_debug.h | 31 +++++++++++++++++++++++++++++++
 src/abm/geography.h | 31 +++++++++++++++++++++++++++++++
 2 files changed, 62 insertions(+)
 create mode 100644 src/abm/abm_debug.h
 create mode 100644 src/abm/geography.h

diff --git a/src/abm/abm_debug.h b/src/abm/abm_debug.h
new file mode 100644
index 000000000..7ef8471a4
--- /dev/null
+++ b/src/abm/abm_debug.h
@@ -0,0 +1,31 @@
+/*******************************************************************************
+ * This file is part of SWIFT_ABM.
+ * Copyright (c) 2025 Dmitry Nikolaenko (dmitry.nikolaenko@durham.ac.uk)
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as published
+ * by the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public License
+ * along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ *
+ ******************************************************************************/
+#ifndef SWIFT_ABM_DEBUG_H
+#define SWIFT_ABM_DEBUG_H
+
+/* Import the right ABM debug variant */
+#if defined(NONE_ABM)
+#include "./None/abm_debug.h"
+#elif defined(HUMANMOBILITY_ABM)
+#include "./HumanMobility/abm_debug.h"
+#else
+#error "Invalid choice of ABM variant"
+#endif
+
+#endif /* SWIFT_ABM_DEBUG_H */
diff --git a/src/abm/geography.h b/src/abm/geography.h
new file mode 100644
index 000000000..52ab82492
--- /dev/null
+++ b/src/abm/geography.h
@@ -0,0 +1,31 @@
+/*******************************************************************************
+ * This file is part of SWIFT_ABM.
+ * Copyright (c) 2025 Dmitry Nikolaenko (dmitry.nikolaenko@durham.ac.uk)
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as published
+ * by the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public License
+ * along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ *
+ ******************************************************************************/
+#ifndef SWIFT_ABM_GEOGRAPHY_H
+#define SWIFT_ABM_GEOGRAPHY_H
+
+/* Import the right geography definition */
+#if defined(HM_CASE_NONE)
+#include "./Geography/none/potential.h"
+#elif defined(HM_CASE_RIVER)
+#include "./Geography/river/potential.h"
+#else
+#error "Invalid choice of geography variant"
+#endif
+
+#endif /* SWIFT_ABM_GEOGRAPHY_H */

From cd2df25471619172ebe7aab24e7b7f6b4d949aba Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 7 Apr 2025 12:41:45 +0100
Subject: [PATCH 05/43] Add the option to run HumanMobility with both random
 walk and river together

---
 configure.ac                                  | 13 ++++-----
 examples/HumanMobility/makeRiver.py           |  2 +-
 .../HumanMobility/plot_velocity_parallel.py   | 10 +++++++
 examples/HumanMobility/run.sh                 |  3 +-
 examples/HumanMobility/visualise.sh           | 12 ++++----
 install_swift.sh                              |  2 +-
 swift.c                                       | 29 +++++++++++++++++++
 7 files changed, 55 insertions(+), 16 deletions(-)

diff --git a/configure.ac b/configure.ac
index 57b1dbebc..6e42ce82f 100644
--- a/configure.ac
+++ b/configure.ac
@@ -2191,7 +2191,7 @@ fi
 # HM case.
 AC_ARG_WITH([hm],
    [AS_HELP_STRING([--with-hm=<case>],
-      [Human mobility scenario @<:@none, random-walk, river default: none@:>@]
+      [Human mobility scenario @<:@none, all, random-walk, river default: none@:>@]
    )],
    [with_hm="$withval"],
    [with_hm="none"]
@@ -2201,18 +2201,17 @@ case "$with_hm" in
    none)
       AC_DEFINE([HM_CASE_NONE], [1], [No HM case])
    ;;
+   "all")
+#      AC_DEFINE([HAVE_HM_ALL], 1, [Have all Human Mobility cases])
+      AC_DEFINE([HM_CASE_RIVER], 1, [HM case River])
+      AC_DEFINE([HM_CASE_RANDOMWALK], 1, [HM case Random Walk])
+   ;;
    random-walk)
       AC_DEFINE([HM_CASE_RANDOMWALK], [1], [HM case Random Walk])
    ;;
    river)
       AC_DEFINE([HM_CASE_RIVER], [1], [HM case River])
    ;;
-   # river)
-   #    AC_DEFINE([EXTERNAL_POTENTIAL_RIVER], [1], [River obstactle.])
-   # ;;
-   # new-case)
-   #    AC_DEFINE([HM_CASE_NEWSCENARIO], [1], [HM case New Scenario]) 
-   # ;;
    *)
       AC_MSG_ERROR([Unknown HM case: $with_hm])
    ;;
diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index 99f9a2d1f..cbf4f3e87 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -13,7 +13,7 @@
 river_y = [5010., 5090.]     # river banks y-coordinates
 river_width = river_y[1] - river_y[0]
 river_center = np.mean(river_y)
-mass = 1e6                 # "mass" of the river
+mass = 1e5                 # "mass" of the river
 distance = 1.0          # minimal distance from the river
 
 # Grid parameters
diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index 46db93f78..c35ea723e 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -16,6 +16,12 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
 
     with h5py.File("river.hdf5", 'r') as sim:
         mass = sim["/Header"].attrs["Mass"]
+        box_size = sim["/Header"].attrs["BoxSize"]  # Add this line
+        
+        # Create horizontal lines for river banks across the full width
+        x_bank = np.array([0, box_size[0]])  # Full width of box
+        y_north = np.array([5090., 5090.])   # Northern bank
+        y_south = np.array([5010., 5010.])   # Southern bank
 
     # Define the filename pattern for the HDF5 files and the PNG files
     filename_hdf5 = "data/humanMobility_%04d.hdf5" % i  # Adjust the filename pattern as needed
@@ -85,6 +91,10 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
         fig, ax = plt.subplots()
         ax.set_title("Velocity map of human mobility %04d (scale=%07.2d)\n \"river mass\"=%s" % (i, scale, mass))
 
+        # Plot river banks before quiver plot
+        ax.plot(x_bank, y_north, '-', color='0.8', linewidth=0.2)  # 0.8 = light gray
+        ax.plot(x_bank, y_south, '-', color='0.8', linewidth=0.2)
+        
         # Create the quiver plot
         Q = ax.quiver(
             X,              # X positions
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index d2d7aa707..cd1fe2b4a 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -23,4 +23,5 @@ fi
 
 # Run SWIFT
 # gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
-swift -A -s -g -G --threads=4 -n 10000 humanMobility.yml #
+#time swift --hm-river --hm-randomwalk --threads=8 -n 10000 humanMobility.yml # -A -s -g -G 
+time mpirun -n 2 swift_mpi -A -s -g -G --hm-river --hm-randomwalk --threads=4 -n 50000 humanMobility.yml #
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index f73baeb07..4b13e16f0 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -2,11 +2,11 @@
 
 rm images/*
 
-num_files=407 #501
-min_x=0
-max_x=10000
-min_y=0
-max_y=10000
+num_files=101
+min_x=4000
+max_x=6000
+min_y=4000
+max_y=6000
 type="gas"  # or "particles"
 
 # Plot the result
@@ -19,7 +19,7 @@ python3 generate_GIF.py ${num_files}
 # - The total number of frames to 75 (covering humanMobility_0000.png to humanMobility_0074.png).
 # - The output video format to H.264 with yuv420p pixel format for broader compatibility.
 
-ffmpeg -framerate 20 \
+ffmpeg -framerate 50 \
        -start_number 0 \
        -i images/humanMobility_%04d.png \
        -frames:v ${num_files} \
diff --git a/install_swift.sh b/install_swift.sh
index dc674ed3c..6b36e91b2 100755
--- a/install_swift.sh
+++ b/install_swift.sh
@@ -26,7 +26,7 @@ export CFLAGS="-fsanitize=address -g"
 	    --with-hydro-dimension=2 \
 		--with-abm=human-mobility \
 		--with-ext-potential=human-mobility \
-	    --with-hm=river
+	    --with-hm=all
 
 	    # --with-hm=random-walk
 
diff --git a/swift.c b/swift.c
index 8a535d664..5fb4e519b 100644
--- a/swift.c
+++ b/swift.c
@@ -196,6 +196,8 @@ int main(int argc, char *argv[]) {
   int with_eagle = 0;
   int with_gear = 0;
   int with_agora = 0;
+  int with_hm_randomwalk = 0;
+  int with_hm_river = 0;
   int with_line_of_sight = 0;
   int with_rt = 0;
   int with_power = 0;
@@ -300,6 +302,16 @@ int main(int argc, char *argv[]) {
           "equivalent to --hydro --limiter --sync --self-gravity --stars "
           "--star-formation --cooling --feedback.",
           NULL, 0, 0),
+      OPT_BOOLEAN(
+          0, "hm-randomwalk", &with_hm_randomwalk,
+          "Run with all the options needed for the random walk model. This is "
+          "equivalent to --hydro --abm.",
+          NULL, 0, 0),
+      OPT_BOOLEAN(
+          0, "hm-river", &with_hm_river,
+          "Run with all the options needed for the river model. This is "
+          "equivalent to --hydro --abm.",
+          NULL, 0, 0),
 
       OPT_GROUP("  Control options:\n"),
       OPT_BOOLEAN('a', "pin", &with_aff,
@@ -413,6 +425,23 @@ int main(int argc, char *argv[]) {
     with_cooling = 1;
     with_feedback = 1;
   }
+  if (with_hm_randomwalk) {
+#ifndef HM_CASE_RANDOMWALK
+    error("SWIFT was compiled without random walk case support. Recompile with --with-hm=random-walk or --with-hm=all"); 
+#endif
+    with_hydro = 1;
+    with_abm = 1;
+    with_self_gravity = 1; // Need this for random walk potential?
+  }
+  if (with_hm_river) {
+#ifndef HM_CASE_RIVER
+    error("SWIFT was compiled without river case support. Recompile with --with-hm=river or --with-hm=all");
+#endif
+    with_hydro = 1;
+    with_abm = 1;
+    with_external_gravity = 1;  // Need this for river potential
+    // with_self_gravity = 1;
+  }
 
   /* Deal with thread numbers */
   if (nr_threads <= 0)

From 9d02838d8d426be1e1c1bd9206667de8d2ad4f4a Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 7 Apr 2025 14:51:09 +0100
Subject: [PATCH 06/43] Small corrections for example simulation (random walk +
 river)

---
 examples/HumanMobility/plot_velocity_parallel.py | 4 ++--
 examples/HumanMobility/visualise.sh              | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index c35ea723e..bd8210926 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -20,8 +20,8 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
         
         # Create horizontal lines for river banks across the full width
         x_bank = np.array([0, box_size[0]])  # Full width of box
-        y_north = np.array([5090., 5090.])   # Northern bank
-        y_south = np.array([5010., 5010.])   # Southern bank
+        y_north = np.array([5080., 5080.])   # Northern bank
+        y_south = np.array([5020., 5020.])   # Southern bank
 
     # Define the filename pattern for the HDF5 files and the PNG files
     filename_hdf5 = "data/humanMobility_%04d.hdf5" % i  # Adjust the filename pattern as needed
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index 4b13e16f0..872a48b61 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -2,11 +2,11 @@
 
 rm images/*
 
-num_files=101
+num_files=501
 min_x=4000
 max_x=6000
-min_y=4000
-max_y=6000
+min_y=4900
+max_y=5200
 type="gas"  # or "particles"
 
 # Plot the result
@@ -19,7 +19,7 @@ python3 generate_GIF.py ${num_files}
 # - The total number of frames to 75 (covering humanMobility_0000.png to humanMobility_0074.png).
 # - The output video format to H.264 with yuv420p pixel format for broader compatibility.
 
-ffmpeg -framerate 50 \
+ffmpeg -framerate 20 \
        -start_number 0 \
        -i images/humanMobility_%04d.png \
        -frames:v ${num_files} \

From 26066789eb9a7a90813936ccabb68ee9042259a4 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 7 Apr 2025 14:58:45 +0100
Subject: [PATCH 07/43] Remove empty function

---
 src/abm/HumanMobility/abm_utils.h | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/src/abm/HumanMobility/abm_utils.h b/src/abm/HumanMobility/abm_utils.h
index 95a4680a4..c604a1479 100644
--- a/src/abm/HumanMobility/abm_utils.h
+++ b/src/abm/HumanMobility/abm_utils.h
@@ -91,15 +91,6 @@ __attribute__((always_inline)) INLINE static void bilinear_interpolation(
 }
 
 
-/**
- * @brief Check if a human is in the river (between the banks for a given position (x, y)).
- *
- * @param 
- */
-__attribute__((always_inline)) INLINE static void check_human_in_river( ) {
-
-}
-
 /**
  * @brief A utility to advance random walk of a human.
  *

From b4f84db7f414da2bc1b4ee693ebc319a6924e3f2 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 28 Apr 2025 15:22:01 +0100
Subject: [PATCH 08/43] Correct distance to river calculation

---
 examples/HumanMobility/makeRiver.py | 4 ++--
 examples/HumanMobility/visualise.sh | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index cbf4f3e87..ad8ed0746 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -10,7 +10,7 @@
 
 # River parameters (matching humanMobility.yml)
 box_size = [10000., 10000.]  # meters
-river_y = [5010., 5090.]     # river banks y-coordinates
+river_y = [5020., 5080.]     # river banks y-coordinates
 river_width = river_y[1] - river_y[0]
 river_center = np.mean(river_y)
 mass = 1e5                 # "mass" of the river
@@ -42,7 +42,7 @@
         dy = y - river_y[1]
     else:
         # Inside the river
-        dy = distance
+        dy = distance if y >= river_center else -distance
     
     # Calculate acceleration using inverse cube law (rinv3)
     r = np.sqrt(dy * dy)
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index 872a48b61..7553d15d5 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -3,10 +3,10 @@
 rm images/*
 
 num_files=501
-min_x=4000
-max_x=6000
-min_y=4900
-max_y=5200
+min_x=3000
+max_x=7000
+min_y=3000
+max_y=7000
 type="gas"  # or "particles"
 
 # Plot the result

From 5b54404c5586a258c5059b7e78828d1ed88d770d Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 28 Apr 2025 19:48:19 +0100
Subject: [PATCH 09/43] Add meandering river

---
 examples/HumanMobility/makeIC.py              |   1 +
 examples/HumanMobility/makeRandomRiver.py     | 155 +++++++++++++++
 examples/HumanMobility/makeRiver.py           | 185 +++++++++++-------
 .../HumanMobility/plot_velocity_parallel.py   |  25 ++-
 examples/HumanMobility/run.sh                 |   3 +-
 5 files changed, 287 insertions(+), 82 deletions(-)
 create mode 100644 examples/HumanMobility/makeRandomRiver.py

diff --git a/examples/HumanMobility/makeIC.py b/examples/HumanMobility/makeIC.py
index 55ab0fe29..50953b6fc 100644
--- a/examples/HumanMobility/makeIC.py
+++ b/examples/HumanMobility/makeIC.py
@@ -58,6 +58,7 @@ def calculate_velocities(self, angle=0):
         v_x = (np.random.rand(self.nhumans)-0.5)*1000 #np.zeros(self.nhumans) #
         v_y = (np.random.rand(self.nhumans)-0.5)*1000 #np.zeros(self.nhumans) #
         v_z = np.zeros(self.nhumans)
+        # print("Velocity means:", v_x.mean(), v_y.mean(), v_z.mean())
 
         self.velocities = np.array([v_x, v_y, v_z]).T
 
diff --git a/examples/HumanMobility/makeRandomRiver.py b/examples/HumanMobility/makeRandomRiver.py
new file mode 100644
index 000000000..2b056ba31
--- /dev/null
+++ b/examples/HumanMobility/makeRandomRiver.py
@@ -0,0 +1,155 @@
+import numpy as np
+import h5py as h5
+import argparse as ap
+from scipy.ndimage import gaussian_filter
+from scipy.interpolate import splprep, splev
+
+def generate_meandering_river(box_size, num_control_points=8, river_width=60.0, randomness=0.15):
+    """Generate a meandering river using control points and spline interpolation"""
+    
+    # Generate random control points for river centerline
+    # Start from left side (west)
+    x_controls = np.linspace(0, box_size[0], num_control_points)
+    
+    # Add random vertical displacement to control points (keeping ends fixed)
+    y_controls = np.zeros(num_control_points)
+    y_controls[1:-1] = box_size[1]/2 + randomness * box_size[1] * np.random.randn(num_control_points-2)
+    y_controls[0] = box_size[1]/2  # Fix start point
+    y_controls[-1] = box_size[1]/2  # Fix end point
+    
+    # Fit a spline through control points
+    tck, u = splprep([x_controls, y_controls], s=0, k=3)
+    
+    # Generate points along the spline for smooth river
+    t_fine = np.linspace(0, 1, 1000)
+    x_center, y_center = splev(t_fine, tck)
+    
+    # Calculate tangent vectors along the river
+    dx = np.gradient(x_center)
+    dy = np.gradient(y_center)
+    
+    # Normalize tangent vectors
+    norm = np.sqrt(dx*dx + dy*dy)
+    dx /= norm
+    dy /= norm
+    
+    # Calculate normal vectors (perpendicular to tangent)
+    normal_x = -dy
+    normal_y = dx
+    
+    # Generate river banks by offsetting perpendicular to centerline
+    half_width = river_width / 2.0
+    left_bank_x = x_center - normal_x * half_width
+    left_bank_y = y_center - normal_y * half_width
+    right_bank_x = x_center + normal_x * half_width
+    right_bank_y = y_center + normal_y * half_width
+    
+    return x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y
+
+def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y, mass, distance):
+    """Calculate acceleration at point (x,y) due to river banks"""
+    
+    # Find distances to nearest points on both banks
+    left_dists = np.sqrt((x - left_bank_x)**2 + (y - left_bank_y)**2)
+    right_dists = np.sqrt((x - right_bank_x)**2 + (y - right_bank_y)**2)
+    
+    min_left_dist = np.min(left_dists)
+    min_right_dist = np.min(right_dists)
+    
+    # Inside river check (if point is closer than distance to both banks)
+    if min_left_dist < distance and min_right_dist < distance:
+        return 0.0, 0.0
+    
+    # Calculate acceleration from both banks
+    ax = 0.0
+    ay = 0.0
+    
+    # Add contribution from nearest points on both banks
+    for bank_x, bank_y, dists in [(left_bank_x, left_bank_y, left_dists),
+                                 (right_bank_x, right_bank_y, right_dists)]:
+        idx = np.argmin(dists)
+        r = max(dists[idx], distance)
+        
+        dx = x - bank_x[idx]
+        dy = y - bank_y[idx]
+        rinv3 = 1.0 / (r * r * r)
+        
+        ax += mass * dx * rinv3
+        ay += mass * dy * rinv3
+    
+    return ax, ay
+
+def main():
+    # Parse arguments
+    parser = ap.ArgumentParser()
+    parser.add_argument("-f", "--file", type=str, default="river.hdf5")
+    parser.add_argument("-s", "--seed", type=int, default=42,
+                       help="Random seed for river generation")
+    args = parser.parse_args()
+    
+    # Set random seed for reproducibility
+    np.random.seed(args.seed)
+    
+    # Parameters
+    box_size = [10000., 10000.]  # meters
+    mass = 1e5                    # "mass" of the river
+    distance = 1.0                # minimal distance from river
+    river_width = 60.0           # width of the river
+    
+    # Grid parameters
+    grid_size = [1001, 1001]     # number of cells + 1
+    
+    # Generate meandering river
+    x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y = \
+        generate_meandering_river(box_size, river_width=river_width)
+    
+    # Create acceleration field arrays
+    ax = np.zeros(grid_size, dtype=np.float32)
+    ay = np.zeros(grid_size, dtype=np.float32)
+    
+    # Generate grid coordinates
+    x_coords = np.linspace(0, box_size[0], grid_size[0])
+    y_coords = np.linspace(0, box_size[1], grid_size[1])
+    
+    # Calculate acceleration field at each grid point
+    print("Calculating acceleration field...")
+    for i, x in enumerate(x_coords):
+        if i % 100 == 0:  # Progress indicator
+            print(f"Processing row {i}/{grid_size[0]}")
+        for j, y in enumerate(y_coords):
+            ax[i,j], ay[i,j] = calculate_river_acceleration(
+                x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y,
+                mass, distance
+            )
+    
+    # Optional: Smooth the acceleration field
+    # print("Smoothing acceleration field...")
+    # ax = gaussian_filter(ax, sigma=1.0)
+    # ay = gaussian_filter(ay, sigma=1.0)
+    
+    # Save to HDF5 file
+    print("Saving to HDF5 file...")
+    with h5.File(args.file, 'w') as f:
+        # Save acceleration field
+        f.create_dataset("AccelerationField/ax", data=ax)
+        f.create_dataset("AccelerationField/ay", data=ay)
+        
+        # Save river geometry for visualization
+        f.create_dataset("RiverGeometry/centerline_x", data=x_center)
+        f.create_dataset("RiverGeometry/centerline_y", data=y_center)
+        f.create_dataset("RiverGeometry/left_bank_x", data=left_bank_x)
+        f.create_dataset("RiverGeometry/left_bank_y", data=left_bank_y)
+        f.create_dataset("RiverGeometry/right_bank_x", data=right_bank_x)
+        f.create_dataset("RiverGeometry/right_bank_y", data=right_bank_y)
+        
+        # Save metadata
+        f.create_group("Header")
+        f["Header"].attrs["BoxSize"] = np.array(box_size, dtype=np.float64)
+        f["Header"].attrs["GridSize"] = np.array(grid_size, dtype=np.int32)
+        f["Header"].attrs["Mass"] = np.array(mass, dtype=np.float64)
+        f["Header"].attrs["Distance"] = np.array(distance, dtype=np.float64)
+        f["Header"].attrs["RiverWidth"] = np.array(river_width, dtype=np.float64)
+        f["Header"].attrs["RandomSeed"] = np.array(args.seed, dtype=np.int32)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index ad8ed0746..4fb671dca 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -3,79 +3,122 @@
 import argparse as ap
 from scipy.ndimage import gaussian_filter
 
-# Parse arguments
-parser = ap.ArgumentParser()
-parser.add_argument("-f", "--file", type=str, default="river.hdf5")
-args = parser.parse_args()
-
-# River parameters (matching humanMobility.yml)
-box_size = [10000., 10000.]  # meters
-river_y = [5020., 5080.]     # river banks y-coordinates
-river_width = river_y[1] - river_y[0]
-river_center = np.mean(river_y)
-mass = 1e5                 # "mass" of the river
-distance = 1.0          # minimal distance from the river
-
-# Grid parameters
-x_grid_size = 1000
-y_grid_size = 1000
-grid_size = [x_grid_size+1, y_grid_size+1]  # number of cells
-
-# Create acceleration field
-ax = np.zeros(grid_size, dtype=np.float32)
-ay = np.zeros(grid_size, dtype=np.float32)
-
-# Generate grid coordinates
-y_coords = np.linspace(0, box_size[1], grid_size[1])
-
-# Set acceleration field
-# ax component is zero outside river
-ax[:, :] = 0
-for i in range(grid_size[1]):
-    y = y_coords[i]
+def generate_river(box_size, river_width=60.0):
+    """Generate a horizontal river with constant width"""
+    
+    # Generate river centerline (straight line from west to east)
+    x_center = np.linspace(0, box_size[0], 1000)
+    y_center = np.ones_like(x_center) * box_size[1]/2
+    
+    # Generate river banks by offsetting from centerline
+    half_width = river_width / 2.0
+    left_bank_x = x_center
+    left_bank_y = y_center - half_width
+    right_bank_x = x_center
+    right_bank_y = y_center + half_width
+    
+    return x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y
 
-### FOR THE RIVER FROM WEST TO EAST (simple example) ###
-    # Determine distance from river
-    if y < river_y[0]-distance:
-        dy = y - river_y[0]
-    elif y > river_y[1]+distance:
-        dy = y - river_y[1]
-    else:
-        # Inside the river
-        dy = distance if y >= river_center else -distance
+def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y, mass, distance):
+    """Calculate acceleration at point (x,y) due to river banks"""
+    
+    # For horizontal river, we only need y-distances
+    dy_left = y - left_bank_y[0]  # y-coordinate is constant for each bank
+    dy_right = y - right_bank_y[0]
+    
+    # Inside river check
+    if abs(dy_left) < distance and abs(dy_right) < distance:
+        return 0.0, 0.0
     
-    # Calculate acceleration using inverse cube law (rinv3)
-    r = np.sqrt(dy * dy)
-    rinv = 1.0 / r
-    rinv3 = rinv * rinv * rinv
+    # Calculate acceleration
+    ax = 0.0
+    ay = 0.0
     
-    # Set acceleration components
-    # ay component follows inverse cube law
-    ay[:, i] = mass * dy * rinv3
+    # Determine closest bank and calculate acceleration
+    if abs(dy_left) < abs(dy_right):
+        # Closer to left bank
+        r = max(abs(dy_left), distance)
+        rinv3 = 1.0 / (r * r * r)
+        ay = mass * dy_left * rinv3
+    else:
+        # Closer to right bank
+        r = max(abs(dy_right), distance)
+        rinv3 = 1.0 / (r * r * r)
+        ay = mass * dy_right * rinv3
+    
+    return ax, ay
 
-# Smooth the acceleration field
-#ax = gaussian_filter(ax, sigma=1.0)
-#ay = gaussian_filter(ay, sigma=1.0)
-### END RIVER ###
+def main():
+    # Parse arguments
+    parser = ap.ArgumentParser()
+    parser.add_argument("-f", "--file", type=str, default="river.hdf5")
+    parser.add_argument("-s", "--seed", type=int, default=42,
+                       help="Random seed for river generation")
+    args = parser.parse_args()
+    
+    # Set random seed for reproducibility
+    np.random.seed(args.seed)
+    
+    # Parameters
+    box_size = [10000., 10000.]  # meters
+    mass = 1e5                    # "mass" of the river
+    distance = 1.0                # minimal distance from river
+    river_width = 60.0           # width of the river
+    
+    # Grid parameters
+    grid_size = [1001, 1001]     # number of cells + 1
+    
+    # Generate straight river
+    x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y = \
+        generate_river(box_size, river_width=river_width)
+    
+    # Create acceleration field arrays
+    ax = np.zeros(grid_size, dtype=np.float32)
+    ay = np.zeros(grid_size, dtype=np.float32)
+    
+    # Generate grid coordinates
+    x_coords = np.linspace(0, box_size[0], grid_size[0])
+    y_coords = np.linspace(0, box_size[1], grid_size[1])
+    
+    # Calculate acceleration field at each grid point
+    print("Calculating acceleration field...")
+    for i, x in enumerate(x_coords):
+        if i % 100 == 0:  # Progress indicator
+            print(f"Processing row {i}/{grid_size[0]}")
+        for j, y in enumerate(y_coords):
+            ax[i,j], ay[i,j] = calculate_river_acceleration(
+                x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y,
+                mass, distance
+            )
+    
+    # Optional: Smooth the acceleration field
+    # print("Smoothing acceleration field...")
+    # ax = gaussian_filter(ax, sigma=1.0)
+    # ay = gaussian_filter(ay, sigma=1.0)
+    
+    # Save to HDF5 file
+    print("Saving to HDF5 file...")
+    with h5.File(args.file, 'w') as f:
+        # Save acceleration field
+        f.create_dataset("AccelerationField/ax", data=ax)
+        f.create_dataset("AccelerationField/ay", data=ay)
+        
+        # Save river geometry for visualization
+        f.create_dataset("RiverGeometry/centerline_x", data=x_center)
+        f.create_dataset("RiverGeometry/centerline_y", data=y_center)
+        f.create_dataset("RiverGeometry/left_bank_x", data=left_bank_x)
+        f.create_dataset("RiverGeometry/left_bank_y", data=left_bank_y)
+        f.create_dataset("RiverGeometry/right_bank_x", data=right_bank_x)
+        f.create_dataset("RiverGeometry/right_bank_y", data=right_bank_y)
+        
+        # Save metadata
+        f.create_group("Header")
+        f["Header"].attrs["BoxSize"] = np.array(box_size, dtype=np.float64)
+        f["Header"].attrs["GridSize"] = np.array(grid_size, dtype=np.int32)
+        f["Header"].attrs["Mass"] = np.array(mass, dtype=np.float64)
+        f["Header"].attrs["Distance"] = np.array(distance, dtype=np.float64)
+        f["Header"].attrs["RiverWidth"] = np.array(river_width, dtype=np.float64)
+        f["Header"].attrs["RandomSeed"] = np.array(args.seed, dtype=np.int32)  # Add this line
 
-# Save to HDF5 file
-with h5.File(args.file, 'w') as f:
-    print(f"Creating acceleration field with shape: {ax.shape}")
-    print(f"Grid size: {grid_size}")
-    print(f"Box size: {box_size}")
-    print(f"Mass: {mass}")
-    
-    f.create_dataset("AccelerationField/ax", data=ax)
-    f.create_dataset("AccelerationField/ay", data=ay)
-    f.create_group("Header")
-    f["Header"].attrs["BoxSize"] = np.array(box_size, dtype=np.float64)
-    f["Header"].attrs["GridSize"] = np.array(grid_size, dtype=np.int32)
-    f["Header"].attrs["Mass"] = np.array(mass, dtype=np.float64)
-    f["Header"].attrs["Distance"] = np.array(distance, dtype=np.float64)
-    
-    # Verify data was written correctly
-    print(f"Wrote ax with shape: {f['AccelerationField/ax'].shape}")
-    print(f"Wrote ay with shape: {f['AccelerationField/ay'].shape}")
-    print(f"Header/BoxSize: {f['Header'].attrs['BoxSize']}")
-    print(f"Header/GridSize: {f['Header'].attrs['GridSize']}")
-    print(f"Header/GridSize: {f['Header'].attrs['Mass']}")
\ No newline at end of file
+if __name__ == "__main__":
+    main()
diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index bd8210926..f507af120 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -16,12 +16,15 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
 
     with h5py.File("river.hdf5", 'r') as sim:
         mass = sim["/Header"].attrs["Mass"]
-        box_size = sim["/Header"].attrs["BoxSize"]  # Add this line
+        box_size = sim["/Header"].attrs["BoxSize"]
         
-        # Create horizontal lines for river banks across the full width
-        x_bank = np.array([0, box_size[0]])  # Full width of box
-        y_north = np.array([5080., 5080.])   # Northern bank
-        y_south = np.array([5020., 5020.])   # Southern bank
+        # Load river geometry from HDF5 file
+        left_bank_x = sim["/RiverGeometry/left_bank_x"][:]
+        left_bank_y = sim["/RiverGeometry/left_bank_y"][:]
+        right_bank_x = sim["/RiverGeometry/right_bank_x"][:]
+        right_bank_y = sim["/RiverGeometry/right_bank_y"][:]
+        center_x = sim["/RiverGeometry/centerline_x"][:]
+        center_y = sim["/RiverGeometry/centerline_y"][:]
 
     # Define the filename pattern for the HDF5 files and the PNG files
     filename_hdf5 = "data/humanMobility_%04d.hdf5" % i  # Adjust the filename pattern as needed
@@ -91,10 +94,11 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
         fig, ax = plt.subplots()
         ax.set_title("Velocity map of human mobility %04d (scale=%07.2d)\n \"river mass\"=%s" % (i, scale, mass))
 
-        # Plot river banks before quiver plot
-        ax.plot(x_bank, y_north, '-', color='0.8', linewidth=0.2)  # 0.8 = light gray
-        ax.plot(x_bank, y_south, '-', color='0.8', linewidth=0.2)
-        
+        # Plot river banks and centerline
+        ax.plot(left_bank_x, left_bank_y, '-', color='0.8', linewidth=0.5, label='Left bank')
+        ax.plot(right_bank_x, right_bank_y, '-', color='0.8', linewidth=0.5, label='Right bank')
+        ax.plot(center_x, center_y, '--', color='0.6', linewidth=0.3, label='River center')
+
         # Create the quiver plot
         Q = ax.quiver(
             X,              # X positions
@@ -124,6 +128,9 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
         cbar = fig.colorbar(Q, ax=ax, label='Velocity Magnitude')
 
         qk = ax.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E', coordinates='figure')
+        # Add legend if desired
+        ax.legend(loc='upper right', fontsize='x-small')
+
         # Add labels and formatting
         # plt.text(
         #     0.97, 0.97, "${\\rm{Velocity~vectors}}$",
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index cd1fe2b4a..d93ab8695 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -1,4 +1,3 @@
-
 #!/bin/bash
 
 # Remove previously generated data and images to regenerate the new ones
@@ -18,7 +17,7 @@ fi
 if [ ! -e river.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRiver.py -f river.hdf5
+    python3 makeRiver.py -f river.hdf5 -s 42
 fi
 
 # Run SWIFT

From 7b561dd8dee8160b0c09922f8819c441ab2d8c24 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 28 Apr 2025 20:34:07 +0100
Subject: [PATCH 10/43] Correct acceleration calculation inside river

---
 examples/HumanMobility/makeRiver.py | 29 ++++++++++++++---------------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index 4fb671dca..9a9056b60 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -26,25 +26,24 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
     dy_left = y - left_bank_y[0]  # y-coordinate is constant for each bank
     dy_right = y - right_bank_y[0]
     
-    # Inside river check
-    if abs(dy_left) < distance and abs(dy_right) < distance:
-        return 0.0, 0.0
-    
     # Calculate acceleration
     ax = 0.0
     ay = 0.0
     
-    # Determine closest bank and calculate acceleration
-    if abs(dy_left) < abs(dy_right):
-        # Closer to left bank
-        r = max(abs(dy_left), distance)
-        rinv3 = 1.0 / (r * r * r)
-        ay = mass * dy_left * rinv3
-    else:
-        # Closer to right bank
-        r = max(abs(dy_right), distance)
-        rinv3 = 1.0 / (r * r * r)
-        ay = mass * dy_right * rinv3
+    # Inside the river
+    if abs(dy_left) < distance and abs(dy_right) < distance:
+        # Determine closest bank and calculate acceleration
+        if abs(dy_left) < abs(dy_right):
+            # Closer to left bank
+            r = max(abs(dy_left), distance)
+            rinv3 = 1.0 / (r * r * r)
+            ay = mass * dy_left * rinv3
+        else:
+            # Closer to right bank
+            r = max(abs(dy_right), distance)
+            rinv3 = 1.0 / (r * r * r)
+            ay = mass * dy_right * rinv3
+# dy = distance if y >= river_center else -distance
     
     return ax, ay
 

From 2d6825090becce507025cea506d406ceacb985e7 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 29 Apr 2025 00:23:09 +0100
Subject: [PATCH 11/43] Correct acceleration calculation inside river again

---
 examples/HumanMobility/makeRiver.py | 50 ++++++++++++++++++++++-------
 1 file changed, 39 insertions(+), 11 deletions(-)

diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index 9a9056b60..a32470bef 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -20,30 +20,58 @@ def generate_river(box_size, river_width=60.0):
     return x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y
 
 def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y, mass, distance):
-    """Calculate acceleration at point (x,y) due to river banks"""
+    """Calculate acceleration at point (x,y) due to river banks
+    
+    For horizontal river:
+    - Inside river: acceleration proportional to distance from centerline
+    - Within 'distance' from banks: acceleration proportional to distance constant
+    - Outside: acceleration proportional to actual distance from closest bank
+    """
     
     # For horizontal river, we only need y-distances
     dy_left = y - left_bank_y[0]  # y-coordinate is constant for each bank
     dy_right = y - right_bank_y[0]
+    river_center = (left_bank_y[0] + right_bank_y[0]) / 2.0
     
     # Calculate acceleration
     ax = 0.0
     ay = 0.0
     
-    # Inside the river
-    if abs(dy_left) < distance and abs(dy_right) < distance:
-        # Determine closest bank and calculate acceleration
+    # Inside river check
+    if dy_left >= 0 and dy_right <= 0:
+        # Inside river - acceleration proportional to distance from centerline
+        dy_center = y - river_center
+        r = distance  # Use constant distance for magnitude
+        rinv3 = 1.0 / (r * r * r)
+        # Direction away from centerline
+        ay = mass * np.sign(dy_center) * distance * rinv3
+    else:
+        # Outside river or near banks
+        # Find distance to closest bank
         if abs(dy_left) < abs(dy_right):
             # Closer to left bank
-            r = max(abs(dy_left), distance)
-            rinv3 = 1.0 / (r * r * r)
-            ay = mass * dy_left * rinv3
+            if abs(dy_left) <= distance:
+                # Within distance constant from bank - use constant force
+                r = distance
+                rinv3 = 1.0 / (r * r * r)
+                ay = mass * np.sign(dy_left) * distance * rinv3
+            else:
+                # Beyond distance constant - use actual distance
+                r = abs(dy_left)
+                rinv3 = 1.0 / (r * r * r)
+                ay = mass * dy_left * rinv3
         else:
             # Closer to right bank
-            r = max(abs(dy_right), distance)
-            rinv3 = 1.0 / (r * r * r)
-            ay = mass * dy_right * rinv3
-# dy = distance if y >= river_center else -distance
+            if abs(dy_right) <= distance:
+                # Within distance constant from bank - use constant force
+                r = distance
+                rinv3 = 1.0 / (r * r * r)
+                ay = mass * np.sign(dy_right) * distance * rinv3
+            else:
+                # Beyond distance constant - use actual distance
+                r = abs(dy_right)
+                rinv3 = 1.0 / (r * r * r)
+                ay = mass * dy_right * rinv3
     
     return ax, ay
 

From 89462ab6deb71a05fdcf9bc9edc240cce8d937c4 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 29 Apr 2025 00:56:21 +0100
Subject: [PATCH 12/43] Minor corrections

---
 examples/HumanMobility/humanMobility.yml | 4 ++--
 examples/HumanMobility/makeRiver.py      | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index 6c675a43f..b166c2fd8 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -69,6 +69,6 @@ Gravity:
 #   # timestep_mult:             0.1  # (Optional) The dimensionless constant C in the time-step condition
 
 RiverPotential:
-  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
-  mass:     1e6  # "Mass" of the "river" (internal units)
+#  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
+#  mass:     1e6  # "Mass" of the "river" (internal units)
   parameter_file:  river.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/makeRiver.py b/examples/HumanMobility/makeRiver.py
index a32470bef..0c3bf1d50 100644
--- a/examples/HumanMobility/makeRiver.py
+++ b/examples/HumanMobility/makeRiver.py
@@ -8,7 +8,7 @@ def generate_river(box_size, river_width=60.0):
     
     # Generate river centerline (straight line from west to east)
     x_center = np.linspace(0, box_size[0], 1000)
-    y_center = np.ones_like(x_center) * box_size[1]/2
+    y_center = np.ones_like(x_center) * 5050.0  # Changed from box_size[1]/2 to 5050
     
     # Generate river banks by offsetting from centerline
     half_width = river_width / 2.0

From 088157a4fe41440802b9f7bb6e676d6aeee3d3b5 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 29 Apr 2025 01:27:19 +0100
Subject: [PATCH 13/43] Reading river parameters fully from river hdf5, not
 from humans hdf5

---
 src/abm/Geography/river/potential.h | 74 ++++++++++++++++++++++++-----
 1 file changed, 62 insertions(+), 12 deletions(-)

diff --git a/src/abm/Geography/river/potential.h b/src/abm/Geography/river/potential.h
index 337ea5112..7241c3cc8 100644
--- a/src/abm/Geography/river/potential.h
+++ b/src/abm/Geography/river/potential.h
@@ -42,7 +42,7 @@
 struct external_potential {
 
   /*! Position of the river (horizontal on the map) */
-  double y[2]; // the northern and southern bank of the river
+  // double y[2]; // the northern and southern bank of the river
 
   /*! Mass */
   double mass;
@@ -52,6 +52,13 @@ struct external_potential {
   float *ay;
   double box_size[2];
   int grid_size[2];
+  
+  /*! River geometry data */
+  float *left_bank_x;   // Left bank x-coordinates
+  float *left_bank_y;   // Left bank y-coordinates
+  float *right_bank_x;  // Right bank x-coordinates
+  float *right_bank_y;  // Right bank y-coordinates
+  int bank_points;      // Number of points defining each bank
 };
 
 
@@ -185,9 +192,10 @@ static INLINE void geography_read_acceleration_field(
   hid_t group_id = H5Gopen(file_id, "Header", H5P_DEFAULT);
   if (group_id < 0) error("unable to open group Header.\n");
 
-  /* Read the arrays */
+  /* Read box size, grid size and mass */
   io_read_array_attribute(group_id, "BoxSize", DOUBLE, potential->box_size, 2);
   io_read_array_attribute(group_id, "GridSize", INT, potential->grid_size, 2);
+  io_read_attribute(group_id, "Mass", DOUBLE, &potential->mass);
 
   /* Close group */
   hid_t status = H5Gclose(group_id);
@@ -211,6 +219,36 @@ static INLINE void geography_read_acceleration_field(
   status = H5Gclose(group_id);
   if (status < 0) error("error closing group.");
 
+  /* Open group for river geometry */
+  group_id = H5Gopen(file_id, "RiverGeometry", H5P_DEFAULT);
+  if (group_id < 0) error("unable to open group RiverGeometry.\n");
+
+  /* Get size of bank arrays */
+  hid_t dataset = H5Dopen(group_id, "left_bank_x", H5P_DEFAULT);
+  if (dataset < 0) error("unable to open dataset left_bank_x.\n");
+  hid_t space = H5Dget_space(dataset);
+  hsize_t dims[1];
+  H5Sget_simple_extent_dims(space, dims, NULL);
+  potential->bank_points = dims[0];
+  H5Sclose(space);
+  H5Dclose(dataset);
+
+  /* Allocate memory for river geometry */
+  potential->left_bank_x = (float*)malloc(potential->bank_points * sizeof(float));
+  potential->left_bank_y = (float*)malloc(potential->bank_points * sizeof(float));
+  potential->right_bank_x = (float*)malloc(potential->bank_points * sizeof(float));
+  potential->right_bank_y = (float*)malloc(potential->bank_points * sizeof(float));
+
+  /* Read river geometry datasets */
+  io_read_array_dataset(group_id, "left_bank_x", FLOAT, potential->left_bank_x, potential->bank_points);
+  io_read_array_dataset(group_id, "left_bank_y", FLOAT, potential->left_bank_y, potential->bank_points);
+  io_read_array_dataset(group_id, "right_bank_x", FLOAT, potential->right_bank_x, potential->bank_points);
+  io_read_array_dataset(group_id, "right_bank_y", FLOAT, potential->right_bank_y, potential->bank_points);
+
+  /* Close river geometry group */
+  status = H5Gclose(group_id);
+  if (status < 0) error("error closing RiverGeometry group.");
+
   /* Close file */
   status = H5Fclose(file_id);
   if (status < 0) error("error closing file.");
@@ -236,16 +274,7 @@ static INLINE void potential_init_backend(
     const struct unit_system* us, const struct space* s,
     struct external_potential* potential) {
 
-  /* Read river banks position */
-  parser_get_param_double_array(parameter_file,
-                                "RiverPotential:position", // northern and southern bank coordinatates
-                                2, potential->y);
-
-  /* Read mass parameter */
-  potential->mass =
-      parser_get_param_double(parameter_file, "RiverPotential:mass");
-
-  /* Load acceleration field data */
+  /* Load acceleration field data and mass parameter */
   geography_read_acceleration_field(parameter_file, potential);
 }
 
@@ -279,6 +308,27 @@ if (potential->ay != NULL) {
   free(potential->ay);
   potential->ay = NULL;
 }
+
+/* Free river geometry arrays if they were allocated */
+if (potential->left_bank_x != NULL) {
+  free(potential->left_bank_x);
+  potential->left_bank_x = NULL;
+}
+
+if (potential->left_bank_y != NULL) {
+  free(potential->left_bank_y);
+  potential->left_bank_y = NULL;
+}
+
+if (potential->right_bank_x != NULL) {
+  free(potential->right_bank_x);
+  potential->right_bank_x = NULL;
+}
+
+if (potential->right_bank_y != NULL) {
+  free(potential->right_bank_y);
+  potential->right_bank_y = NULL;
+}
 #endif
 
 }

From 7fefac661f75f134d965dd7497cfea7d692d7d4e Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 29 Apr 2025 02:25:19 +0100
Subject: [PATCH 14/43] Adapt meandering river as it was done for straight
 river

---
 examples/HumanMobility/makeRandomRiver.py | 83 +++++++++++++++++------
 examples/HumanMobility/run.sh             |  4 +-
 2 files changed, 66 insertions(+), 21 deletions(-)

diff --git a/examples/HumanMobility/makeRandomRiver.py b/examples/HumanMobility/makeRandomRiver.py
index 2b056ba31..1e06f04e2 100644
--- a/examples/HumanMobility/makeRandomRiver.py
+++ b/examples/HumanMobility/makeRandomRiver.py
@@ -47,35 +47,80 @@ def generate_meandering_river(box_size, num_control_points=8, river_width=60.0,
     return x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y
 
 def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y, mass, distance):
-    """Calculate acceleration at point (x,y) due to river banks"""
+    """Calculate acceleration at point (x,y) due to river banks
     
-    # Find distances to nearest points on both banks
+    For meandering river:
+    - Inside river: acceleration proportional to distance from centerline
+    - Within 'distance' from banks: acceleration proportional to distance constant
+    - Outside: acceleration proportional to actual distance from closest bank
+    """
+    
+    # Find distances and closest points on both banks
     left_dists = np.sqrt((x - left_bank_x)**2 + (y - left_bank_y)**2)
     right_dists = np.sqrt((x - right_bank_x)**2 + (y - right_bank_y)**2)
     
-    min_left_dist = np.min(left_dists)
-    min_right_dist = np.min(right_dists)
+    left_idx = np.argmin(left_dists)
+    right_idx = np.argmin(right_dists)
+    min_left_dist = left_dists[left_idx]
+    min_right_dist = right_dists[right_idx]
     
-    # Inside river check (if point is closer than distance to both banks)
-    if min_left_dist < distance and min_right_dist < distance:
-        return 0.0, 0.0
+    # Calculate center point between closest bank points
+    center_x = (left_bank_x[left_idx] + right_bank_x[right_idx]) / 2
+    center_y = (left_bank_y[left_idx] + right_bank_y[right_idx]) / 2
     
-    # Calculate acceleration from both banks
+    # Calculate acceleration
     ax = 0.0
     ay = 0.0
     
-    # Add contribution from nearest points on both banks
-    for bank_x, bank_y, dists in [(left_bank_x, left_bank_y, left_dists),
-                                 (right_bank_x, right_bank_y, right_dists)]:
-        idx = np.argmin(dists)
-        r = max(dists[idx], distance)
-        
-        dx = x - bank_x[idx]
-        dy = y - bank_y[idx]
+    # Inside river check (if between banks)
+    if min_left_dist <= distance and min_right_dist <= distance:
+        # Inside river - acceleration proportional to distance from centerline
+        dx = x - center_x
+        dy = y - center_y
+        r = distance  # Use constant distance for magnitude
         rinv3 = 1.0 / (r * r * r)
-        
-        ax += mass * dx * rinv3
-        ay += mass * dy * rinv3
+        # Direction away from centerline
+        norm = np.sqrt(dx*dx + dy*dy)
+        if norm > 0:
+            ax = mass * (dx/norm) * distance * rinv3
+            ay = mass * (dy/norm) * distance * rinv3
+    else:
+        # Outside river or near banks
+        # Find closest bank
+        if min_left_dist < min_right_dist:
+            # Closer to left bank
+            dx = x - left_bank_x[left_idx]
+            dy = y - left_bank_y[left_idx]
+            if min_left_dist <= distance:
+                # Within distance constant from bank - use constant force
+                r = distance
+                rinv3 = 1.0 / (r * r * r)
+                norm = np.sqrt(dx*dx + dy*dy)
+                ax = mass * (dx/norm) * distance * rinv3
+                ay = mass * (dy/norm) * distance * rinv3
+            else:
+                # Beyond distance constant - use actual distance
+                r = min_left_dist
+                rinv3 = 1.0 / (r * r * r)
+                ax = mass * dx * rinv3
+                ay = mass * dy * rinv3
+        else:
+            # Closer to right bank
+            dx = x - right_bank_x[right_idx]
+            dy = y - right_bank_y[right_idx]
+            if min_right_dist <= distance:
+                # Within distance constant from bank - use constant force
+                r = distance
+                rinv3 = 1.0 / (r * r * r)
+                norm = np.sqrt(dx*dx + dy*dy)
+                ax = mass * (dx/norm) * distance * rinv3
+                ay = mass * (dy/norm) * distance * rinv3
+            else:
+                # Beyond distance constant - use actual distance
+                r = min_right_dist
+                rinv3 = 1.0 / (r * r * r)
+                ax = mass * dx * rinv3
+                ay = mass * dy * rinv3
     
     return ax, ay
 
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index d93ab8695..1f1d62f5d 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -17,10 +17,10 @@ fi
 if [ ! -e river.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRiver.py -f river.hdf5 -s 42
+    python3 makeRandomRiver.py -f river.hdf5 -s 42
 fi
 
 # Run SWIFT
 # gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
 #time swift --hm-river --hm-randomwalk --threads=8 -n 10000 humanMobility.yml # -A -s -g -G 
-time mpirun -n 2 swift_mpi -A -s -g -G --hm-river --hm-randomwalk --threads=4 -n 50000 humanMobility.yml #
+time mpirun -n 4 swift_mpi -A -s -g -G --hm-river --hm-randomwalk --threads=4 -n 50000 humanMobility.yml #

From 4345be693d40ebdd60f957b8f8bac253d27e2ed6 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 29 Apr 2025 18:07:03 +0100
Subject: [PATCH 15/43] Attempt to fix condition inside meandering river

---
 examples/HumanMobility/makeRandomRiver.py     | 36 +++++++++++++------
 .../HumanMobility/plot_velocity_parallel.py   |  2 +-
 2 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/examples/HumanMobility/makeRandomRiver.py b/examples/HumanMobility/makeRandomRiver.py
index 1e06f04e2..88852e907 100644
--- a/examples/HumanMobility/makeRandomRiver.py
+++ b/examples/HumanMobility/makeRandomRiver.py
@@ -64,17 +64,29 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
     min_left_dist = left_dists[left_idx]
     min_right_dist = right_dists[right_idx]
     
-    # Calculate center point between closest bank points
-    center_x = (left_bank_x[left_idx] + right_bank_x[right_idx]) / 2
-    center_y = (left_bank_y[left_idx] + right_bank_y[right_idx]) / 2
-    
-    # Calculate acceleration
+    # Calculate vectors from banks to point
+    dx_left = x - left_bank_x[left_idx]
+    dy_left = y - left_bank_y[left_idx]
+    dx_right = x - right_bank_x[right_idx]
+    dy_right = y - right_bank_y[right_idx]
+
+    # Calculate normal vectors to check if point is between banks
+    normal_x = right_bank_x[right_idx] - left_bank_x[left_idx]
+    normal_y = right_bank_y[right_idx] - left_bank_y[left_idx]
+    norm = np.sqrt(normal_x*normal_x + normal_y*normal_y)
+    normal_x /= norm
+    normal_y /= norm
+
     ax = 0.0
     ay = 0.0
-    
+
     # Inside river check (if between banks)
-    if min_left_dist <= distance and min_right_dist <= distance:
+    dot_left = dx_left*normal_x + dy_left*normal_y
+    dot_right = dx_right*normal_x + dy_right*normal_y
+    if dot_left >= 0 and dot_right <= 0:  # This checks if point is between banks
         # Inside river - acceleration proportional to distance from centerline
+        center_x = (left_bank_x[left_idx] + right_bank_x[right_idx]) / 2
+        center_y = (left_bank_y[left_idx] + right_bank_y[right_idx]) / 2
         dx = x - center_x
         dy = y - center_y
         r = distance  # Use constant distance for magnitude
@@ -102,8 +114,9 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
                 # Beyond distance constant - use actual distance
                 r = min_left_dist
                 rinv3 = 1.0 / (r * r * r)
-                ax = mass * dx * rinv3
-                ay = mass * dy * rinv3
+                norm = np.sqrt(dx*dx + dy*dy)
+                ax = mass * (dx/norm) * r * rinv3
+                ay = mass * (dy/norm) * r * rinv3
         else:
             # Closer to right bank
             dx = x - right_bank_x[right_idx]
@@ -119,8 +132,9 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
                 # Beyond distance constant - use actual distance
                 r = min_right_dist
                 rinv3 = 1.0 / (r * r * r)
-                ax = mass * dx * rinv3
-                ay = mass * dy * rinv3
+                norm = np.sqrt(dx*dx + dy*dy)
+                ax = mass * (dx/norm) * r * rinv3
+                ay = mass * (dy/norm) * r * rinv3
     
     return ax, ay
 
diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index f507af120..7ba0ef826 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -140,7 +140,7 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
         plt.ylabel("${\\rm{Position}}~y$", labelpad=0)
         plt.xlim(min_x, max_x)
         plt.ylim(min_y, max_y)
-        # plt.tight_layout()
+        plt.tight_layout()
 
         plt.savefig(filename_png, dpi=200)
         plt.close()

From 8fbf19b3a08bf5ea80780700023833789ad3db62 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 29 Apr 2025 22:34:07 +0100
Subject: [PATCH 16/43] Minor corrections, adaptation for Cosma, increasing
 simulation area

---
 examples/HumanMobility/humanMobility.yml      |  3 +-
 examples/HumanMobility/makeIC.py              |  5 +-
 examples/HumanMobility/makeRandomRiver.py     |  6 +-
 .../HumanMobility/plot_velocity_parallel.py   |  2 +-
 examples/HumanMobility/run.sh                 | 43 ++++++++++++--
 examples/HumanMobility/visualise.sh           | 58 ++++++++++++++-----
 install_swift.sh                              | 49 ++++++++++++----
 7 files changed, 131 insertions(+), 35 deletions(-)

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index b166c2fd8..f2d65c864 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -9,7 +9,7 @@ InternalUnitSystem:
 # Parameters governing the time integration
 TimeIntegration:
   time_begin: 0.    # The starting time of the simulation (in internal units).
-  time_end:   5 #43200 # minutes in a month  # The end time of the simulation (in internal units).
+  time_end:   10 #43200 # minutes in a month  # The end time of the simulation (in internal units).
   dt_min:     1e-7     # minute #1e-5  # The minimal time-step size of the simulation (in internal units).
   dt_max:     1e-1 #1440   # The maximal time-step size of the simulation (in internal units).
 
@@ -32,6 +32,7 @@ SPH:
 Scheduler:
   max_top_level_cells: 3
   tasks_per_cell: 64
+  # links_per_tasks: 512
 
 # Parameters related to the initial conditions
 InitialConditions:
diff --git a/examples/HumanMobility/makeIC.py b/examples/HumanMobility/makeIC.py
index 50953b6fc..df5983961 100644
--- a/examples/HumanMobility/makeIC.py
+++ b/examples/HumanMobility/makeIC.py
@@ -108,10 +108,13 @@ def save_to_gadget(self, filename, boxsize=10000, type="particles"):
         mass_table = np.zeros(6)
         mass_table[type_index] = self.humanmass
 
+        # type(boxsize)
+        print("boxsize:", float(boxsize))
+
         with h5.File(filename, "w") as handle:
             wg.write_header(
                 handle,
-                boxsize=[boxsize, boxsize],
+                boxsize=[float(boxsize), float(boxsize)],
                 flag_entropy=0,
                 np_total=np_total,
                 np_total_hw=np.array([0, 0, 0, 0, 0, 0]),
diff --git a/examples/HumanMobility/makeRandomRiver.py b/examples/HumanMobility/makeRandomRiver.py
index 88852e907..accefd061 100644
--- a/examples/HumanMobility/makeRandomRiver.py
+++ b/examples/HumanMobility/makeRandomRiver.py
@@ -150,10 +150,10 @@ def main():
     np.random.seed(args.seed)
     
     # Parameters
-    box_size = [10000., 10000.]  # meters
-    mass = 1e5                    # "mass" of the river
+    box_size = [20000., 20000.]  # meters
+    mass = 1e4                    # "mass" of the river
     distance = 1.0                # minimal distance from river
-    river_width = 60.0           # width of the river
+    river_width = 200.0           # width of the river
     
     # Grid parameters
     grid_size = [1001, 1001]     # number of cells + 1
diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index 7ba0ef826..23c08ed0d 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -74,7 +74,7 @@ def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
         V = vel[:, 1]
 
         # Plot the interesting quantities
-        plt.figure(figsize=(7, 7 / 1.6))
+        plt.figure(figsize=(20, 20 / 1.6))
 
         # Calculate the magnitude of the velocity vectors
         # vel_mag = np.sqrt(vel[:, 0]**2 + vel[:, 1]**2)
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 1f1d62f5d..9f6e13144 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -1,26 +1,59 @@
 #!/bin/bash
+#SBATCH --ntasks=8             # or 32; total number of MPI tasks (cores)
+#SBATCH --nodes=1                       # or 1; number of nodes
+#SBATCH --ntasks-per-node=8 # or 32; MPI tasks per node
+#SBATCH --cpus-per-task=16  # CPU cores per MPI rank
+#SBATCH --mem=32G                      # or 120G; memory per node
+#SBATCH -J hm-run
+#SBATCH -o hm.out
+#SBATCH -e hm.err
+#SBATCH -p cosma5                       # cosma5 partition
+#SBATCH -A durham
+#SBATCH -t 0:30:00
+#SBATCH --mail-type=END
+#SBATCH --mail-user=lcgk69@durham.ac.uk
+
+module purge
+
+module load cosma
+module load intel_comp/2024.2.0 compiler-rt tbb compiler
+#module load compiler-rt tbb compiler mpi
+module load openmpi/5.0.3
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+module load python
 
 # Remove previously generated data and images to regenerate the new ones
 rm humans.hdf5
 rm river.hdf5
 rm data/*
-#rm images/*
+rm images/*
 
 # Generate the initial conditions if they are not present.
 if [ ! -e humans.hdf5 ]
 then
     echo "Generating initial conditions for the human mobility box example..."
-    python3 makeIC.py -t gas -f humans.hdf5  # -t particles
+    # python3 makeIC.py -t gas -f humans.hdf5  # -t particles
+    python3 makeIC.py -t gas -n 50 -b 20000 -f humans.hdf5  # -t particles
 fi
 
 # Generate acceleration field for river
 if [ ! -e river.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRandomRiver.py -f river.hdf5 -s 42
+    python3 makeRandomRiver.py -f river.hdf5
 fi
 
+# Ensure all nodes can access the HDF5 files
+#sync
+#sleep 2  # Give filesystem time to sync
+
+# ulimit -s unlimited
+
 # Run SWIFT
 # gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
-#time swift --hm-river --hm-randomwalk --threads=8 -n 10000 humanMobility.yml # -A -s -g -G 
-time mpirun -n 4 swift_mpi -A -s -g -G --hm-river --hm-randomwalk --threads=4 -n 50000 humanMobility.yml #
+# swift -A -s -g -G --hm-river --hm-randomwalk --threads=8 -n 50000 humanMobility.yml # -A -s -g -G 
+mpirun --bind-to none -np 8 swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 100000 humanMobility.yml
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index 7553d15d5..d3e0fe358 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -1,17 +1,47 @@
 #!/bin/bash
 
+#SBATCH --ntasks=16             # or 32; total number of MPI tasks (cores)
+#SBATCH --nodes=1                       # or 1; number of nodes
+#SBATCH --ntasks-per-node=16 # or 32; MPI tasks per node
+#SBATCH --cpus-per-task=16  # CPU cores per MPI rank
+#SBATCH --mem=32G                      # or 120G; memory per node
+#SBATCH -J hm-vis
+#SBATCH -o hm.out
+#SBATCH -e hm.err
+#SBATCH -p cosma5
+#SBATCH -A durham
+#SBATCH -t 0:30:00
+#SBATCH --mail-type=END
+#SBATCH --mail-user=lcgk69@durham.ac.uk
+
+module purge
+module load cosma
+# module load gnu_comp/14.1.0
+module load intel_comp/2024.2.0 compiler-rt tbb compiler
+module load openmpi/5.0.3
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+
+module load ffmpeg
+
+# Increase file descriptor limit
+ulimit -n 4096  # Adjust this number as needed
+
 rm images/*
 
-num_files=501
-min_x=3000
-max_x=7000
-min_y=3000
-max_y=7000
+num_files=1001
+min_x=0
+max_x=20000
+min_y=0
+max_y=20000
 type="gas"  # or "particles"
 
 # Plot the result
 python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type}
-python3 generate_GIF.py ${num_files}
+# python3 generate_GIF.py ${num_files}
 
 # This command sets:
 # - A framerate of 20 frames per second.
@@ -19,11 +49,11 @@ python3 generate_GIF.py ${num_files}
 # - The total number of frames to 75 (covering humanMobility_0000.png to humanMobility_0074.png).
 # - The output video format to H.264 with yuv420p pixel format for broader compatibility.
 
-ffmpeg -framerate 20 \
-       -start_number 0 \
-       -i images/humanMobility_%04d.png \
-       -frames:v ${num_files} \
-       -y \
-       -c:v libx264 \
-       -pix_fmt yuv420p \
-       video.mp4
+# ffmpeg -framerate 60 \
+#        -start_number 0 \
+#        -i images/humanMobility_%04d.png \
+#        -frames:v ${num_files} \
+#        -y \
+#        -c:v libx264 \
+#        -pix_fmt yuv420p \
+#        video.mp4
diff --git a/install_swift.sh b/install_swift.sh
index 6b36e91b2..1ae552b15 100755
--- a/install_swift.sh
+++ b/install_swift.sh
@@ -1,9 +1,37 @@
 #!/bin/bash
 
+#SBATCH --nodes=1                  # Request 1 node
+#SBATCH --ntasks=1                # Single task for compilation
+#SBATCH --cpus-per-task=16        # Use all cores for parallel make
+#SBATCH --mem=64G                 # Memory for compilation
+#SBATCH -J sw-build              # Job name
+#SBATCH -o sw.out               # Output file
+#SBATCH -e sw.err               # Error file
+#SBATCH -p cosma5               # COSMA5 partition
+#SBATCH -A durham               # Account
+#SBATCH -t 0:30:00             # Increased time for compilation
+
+source ~/swift-env/bin/activate
+
 # Adapt the following lines to your system
-LOCAL_LIBRARY_PATH=/home/dmitry/local/lib
-METIS_PATH=/usr/lib/x86_64-linux-gnu
-PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
+#LOCAL_LIBRARY_PATH=/home/dmitry/local/lib
+#METIS_PATH=/usr/lib/x86_64-linux-gnu
+#PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
+
+module purge
+# module load gnu_comp/14.1.0
+module load intel_comp/2024.2.0 compiler-rt tbb compiler
+module load openmpi/5.0.3
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+module load ffmpeg
+module load python
+
+# Set number of threads for make
+export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
 
 # The main installation script for SWIFT_ABM
 
@@ -15,13 +43,14 @@ make clean
 echo "#############################"
 echo "# Running './configure' ... #"
 echo "#############################"
-export CFLAGS="-fsanitize=address -g"
-./configure CC=gcc \
-	    LDFLAGS=-L${LOCAL_LIBRARY_PATH} \
+#export CFLAGS="-fsanitize=address -g"
+./configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
+	    LDFLAGS=\
 	    --enable-mpi \
+		--enable-ipo \
 	    --enable-parallel-hdf5 \
-	    --with-metis=${METIS_PATH} \
-	    --with-parmetis=${PARMETIS_PATH} \
+		--with-tbbmalloc \
+	    --with-parmetis \
 	    --with-hydro=abm \
 	    --with-hydro-dimension=2 \
 		--with-abm=human-mobility \
@@ -34,7 +63,7 @@ export CFLAGS="-fsanitize=address -g"
 echo "########################################"
 echo "# Running 'make -j\$(nproc --all)' ... #"
 echo "########################################"
-make -j$(nproc --all)
+make
 
 echo "#####################################"
 echo "# Running 'ranlib' on libraries ... #"
@@ -49,4 +78,4 @@ make
 echo "###################################"
 echo "# Running 'sudo make install' ... #"
 echo "###################################"
-sudo make install
+make install

From 0efee9160f27273d2ad7e6cc74687448ff17f2b1 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 6 May 2025 17:48:55 +0100
Subject: [PATCH 17/43] Further adaptations for both locally and Cosma

---
 examples/HumanMobility/humanMobility.yml  |  6 +--
 examples/HumanMobility/job.sh             | 56 +++++++++++++++++++++++
 examples/HumanMobility/makeRandomRiver.py | 11 +++--
 examples/HumanMobility/run.sh             | 36 +++------------
 examples/HumanMobility/submit.sh          | 14 ++++++
 examples/HumanMobility/visualise.sh       | 40 ++--------------
 install_swift.sh                          | 16 ++++---
 7 files changed, 102 insertions(+), 77 deletions(-)
 create mode 100644 examples/HumanMobility/job.sh
 create mode 100755 examples/HumanMobility/submit.sh

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index f2d65c864..fa63b2df9 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -18,7 +18,7 @@ Snapshots:
   subdir:              data
   basename:            humanMobility # Common part of the name of output files
   time_first:          0.         # Time of the first output (in internal units)
-  delta_time:          1e-2 #1440        # Time difference between consecutive outputs (in internal units)
+  delta_time:          1.0 #1e-2 #1440        # Time difference between consecutive outputs (in internal units)
 
 # Parameters governing the conserved quantities statistics
 Statistics:
@@ -30,9 +30,9 @@ SPH:
   CFL_condition:         0.1      # Courant-Friedrich-Levy condition for time integration.
 
 Scheduler:
-  max_top_level_cells: 3
+  max_top_level_cells: 16
   tasks_per_cell: 64
-  # links_per_tasks: 512
+  # links_per_tasks: 1024
 
 # Parameters related to the initial conditions
 InitialConditions:
diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
new file mode 100644
index 000000000..a94e7e6f2
--- /dev/null
+++ b/examples/HumanMobility/job.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+
+#SBATCH --ntasks=16             # Total number of MPI tasks (cores) (max 32)
+#SBATCH --nodes=1               # Number of nodes
+#SBATCH --ntasks-per-node=16    # MPI tasks per node (max 32)
+#SBATCH --cpus-per-task=16      # CPU cores per MPI rank
+#SBATCH --mem=32G               # Memory per node
+#SBATCH -p cosma5               # COSMA5 partition
+#SBATCH -A durham               # Account
+#SBATCH -t 1:00:00
+#SBATCH --mail-type=END
+#SBATCH --mail-user=lcgk69@durham.ac.uk
+
+# Check if an argument was provided
+if [ $# -ne 1 ]; then
+    echo "Usage: $0 [--run|--vis]"
+    exit 1
+fi
+
+mode=$1
+
+case $mode in
+    --run|--vis)
+        ;;
+    *)
+        echo "Invalid argument. Use --run or --vis"
+        exit 1
+        ;;
+esac
+
+# Common module loading for both modes
+module purge
+module load cosma
+module load intel_comp/2025.0.1 # gnu_comp/14.1.0
+module load umf compiler-rt tbb compiler mpi # openmpi/5.0.3
+module load python
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+
+# Additional module for visualisation
+if [ "$mode" = "--vis" ]; then
+    module load ffmpeg
+fi
+
+# Execute the appropriate script
+case $mode in
+    --run)
+        ./run.sh
+        ;;
+    --vis)
+        ./visualise.sh
+        ;;
+esac
diff --git a/examples/HumanMobility/makeRandomRiver.py b/examples/HumanMobility/makeRandomRiver.py
index accefd061..224d31460 100644
--- a/examples/HumanMobility/makeRandomRiver.py
+++ b/examples/HumanMobility/makeRandomRiver.py
@@ -141,22 +141,27 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
 def main():
     # Parse arguments
     parser = ap.ArgumentParser()
-    parser.add_argument("-f", "--file", type=str, default="river.hdf5")
+    parser.add_argument("-f", "--file", type=str, default="river.hdf5",
+                       help="Output HDF5 file name")
     parser.add_argument("-s", "--seed", type=int, default=42,
                        help="Random seed for river generation")
+    parser.add_argument("-b", "--box-size", type=float, default=10000.,
+                       help="Box size in meters (square domain)")
+    parser.add_argument("-g", "--grid-size", type=int, default=1000,
+                       help="Number of grid cells in each dimension + 1")
     args = parser.parse_args()
     
     # Set random seed for reproducibility
     np.random.seed(args.seed)
     
     # Parameters
-    box_size = [20000., 20000.]  # meters
+    box_size = [args.box_size, args.box_size]  # square domain
     mass = 1e4                    # "mass" of the river
     distance = 1.0                # minimal distance from river
     river_width = 200.0           # width of the river
     
     # Grid parameters
-    grid_size = [1001, 1001]     # number of cells + 1
+    grid_size = [args.grid_size+1, args.grid_size+1]  # square grid
     
     # Generate meandering river
     x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y = \
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 9f6e13144..3d6cd15c8 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -1,30 +1,4 @@
 #!/bin/bash
-#SBATCH --ntasks=8             # or 32; total number of MPI tasks (cores)
-#SBATCH --nodes=1                       # or 1; number of nodes
-#SBATCH --ntasks-per-node=8 # or 32; MPI tasks per node
-#SBATCH --cpus-per-task=16  # CPU cores per MPI rank
-#SBATCH --mem=32G                      # or 120G; memory per node
-#SBATCH -J hm-run
-#SBATCH -o hm.out
-#SBATCH -e hm.err
-#SBATCH -p cosma5                       # cosma5 partition
-#SBATCH -A durham
-#SBATCH -t 0:30:00
-#SBATCH --mail-type=END
-#SBATCH --mail-user=lcgk69@durham.ac.uk
-
-module purge
-
-module load cosma
-module load intel_comp/2024.2.0 compiler-rt tbb compiler
-#module load compiler-rt tbb compiler mpi
-module load openmpi/5.0.3
-module load fftw/3.3.10
-module load gsl
-module load parmetis/4.0.3-64bit
-module load parallel_hdf5/1.14.4
-module load sundials/5.8.0_c8_single
-module load python
 
 # Remove previously generated data and images to regenerate the new ones
 rm humans.hdf5
@@ -37,14 +11,14 @@ if [ ! -e humans.hdf5 ]
 then
     echo "Generating initial conditions for the human mobility box example..."
     # python3 makeIC.py -t gas -f humans.hdf5  # -t particles
-    python3 makeIC.py -t gas -n 50 -b 20000 -f humans.hdf5  # -t particles
+    python3 makeIC.py -t gas -n 100 -b 10000 -f humans.hdf5  # -t particles
 fi
 
 # Generate acceleration field for river
 if [ ! -e river.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRandomRiver.py -f river.hdf5
+    python3 makeRandomRiver.py -b 10000 -g 1001 -f river.hdf5
 fi
 
 # Ensure all nodes can access the HDF5 files
@@ -55,5 +29,7 @@ fi
 
 # Run SWIFT
 # gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
-# swift -A -s -g -G --hm-river --hm-randomwalk --threads=8 -n 50000 humanMobility.yml # -A -s -g -G 
-mpirun --bind-to none -np 8 swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 100000 humanMobility.yml
+# swift --hm-river --hm-randomwalk --threads=8 -n 1000 humanMobility.yml # -A -s -g -G 
+mpirun -np 8 swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 10000 humanMobility.yml #--bind-to none 
+# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml # -A -s -g -G
+# likwid-perfctr -a
\ No newline at end of file
diff --git a/examples/HumanMobility/submit.sh b/examples/HumanMobility/submit.sh
new file mode 100755
index 000000000..ae1baadd1
--- /dev/null
+++ b/examples/HumanMobility/submit.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+if [ $# -ne 1 ]; then
+    echo "Usage: $0 [--run|--vis]"
+    exit 1
+fi
+
+mode=$1
+name=${mode#--}  # Remove leading -- from mode
+
+sbatch --job-name="hm-$name" \
+       --output="hm-$name.out" \
+       --error="hm-$name.err" \
+       job.sh $mode
\ No newline at end of file
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index d3e0fe358..fa2fffa51 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -1,36 +1,6 @@
 #!/bin/bash
 
-#SBATCH --ntasks=16             # or 32; total number of MPI tasks (cores)
-#SBATCH --nodes=1                       # or 1; number of nodes
-#SBATCH --ntasks-per-node=16 # or 32; MPI tasks per node
-#SBATCH --cpus-per-task=16  # CPU cores per MPI rank
-#SBATCH --mem=32G                      # or 120G; memory per node
-#SBATCH -J hm-vis
-#SBATCH -o hm.out
-#SBATCH -e hm.err
-#SBATCH -p cosma5
-#SBATCH -A durham
-#SBATCH -t 0:30:00
-#SBATCH --mail-type=END
-#SBATCH --mail-user=lcgk69@durham.ac.uk
-
-module purge
-module load cosma
-# module load gnu_comp/14.1.0
-module load intel_comp/2024.2.0 compiler-rt tbb compiler
-module load openmpi/5.0.3
-module load fftw/3.3.10
-module load gsl
-module load parmetis/4.0.3-64bit
-module load parallel_hdf5/1.14.4
-module load sundials/5.8.0_c8_single
-
-module load ffmpeg
-
-# Increase file descriptor limit
-ulimit -n 4096  # Adjust this number as needed
-
-rm images/*
+rm images/* # don't remove the images directory if locally
 
 num_files=1001
 min_x=0
@@ -40,7 +10,7 @@ max_y=20000
 type="gas"  # or "particles"
 
 # Plot the result
-python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type}
+python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type} # comment this line if locally
 # python3 generate_GIF.py ${num_files}
 
 # This command sets:
@@ -49,11 +19,11 @@ python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_
 # - The total number of frames to 75 (covering humanMobility_0000.png to humanMobility_0074.png).
 # - The output video format to H.264 with yuv420p pixel format for broader compatibility.
 
-# ffmpeg -framerate 60 \
+# ffmpeg -framerate 100 \ # uncomment this line if locally
 #        -start_number 0 \
-#        -i images/humanMobility_%04d.png \
+#        -i cosma/images/humanMobility_%04d.png \
 #        -frames:v ${num_files} \
 #        -y \
 #        -c:v libx264 \
 #        -pix_fmt yuv420p \
-#        video.mp4
+#        cosma/video.mp4
diff --git a/install_swift.sh b/install_swift.sh
index 1ae552b15..94c4491ab 100755
--- a/install_swift.sh
+++ b/install_swift.sh
@@ -19,16 +19,17 @@ source ~/swift-env/bin/activate
 #PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
 
 module purge
-# module load gnu_comp/14.1.0
-module load intel_comp/2024.2.0 compiler-rt tbb compiler
-module load openmpi/5.0.3
+module load intel_comp/2025.0.1 #intel_comp/2024.2.0
+module load umf compiler-rt tbb compiler mpi
+# module load openmpi/5.0.3
+module load python
 module load fftw/3.3.10
 module load gsl
 module load parmetis/4.0.3-64bit
 module load parallel_hdf5/1.14.4
 module load sundials/5.8.0_c8_single
 module load ffmpeg
-module load python
+# module load likwid/5.4.1
 
 # Set number of threads for make
 export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
@@ -43,11 +44,13 @@ make clean
 echo "#############################"
 echo "# Running './configure' ... #"
 echo "#############################"
+# /configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
 #export CFLAGS="-fsanitize=address -g"
-./configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
+./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
+		CFLAGS="-Wno-error=gnu-folding-constant" \
 	    LDFLAGS=\
+		--enable-debug \
 	    --enable-mpi \
-		--enable-ipo \
 	    --enable-parallel-hdf5 \
 		--with-tbbmalloc \
 	    --with-parmetis \
@@ -57,6 +60,7 @@ echo "#############################"
 		--with-ext-potential=human-mobility \
 	    --with-hm=all
 
+		# --enable-ipo \
 	    # --with-hm=random-walk
 
 

From 00b49bf71dfd8f5dd398d6dd33391f92175ad189 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 6 May 2025 18:34:14 +0100
Subject: [PATCH 18/43] Minor correction in run.sh

---
 examples/HumanMobility/run.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 3d6cd15c8..5f84f7f19 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -18,7 +18,7 @@ fi
 if [ ! -e river.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRandomRiver.py -b 10000 -g 1001 -f river.hdf5
+    python3 makeRandomRiver.py -b 10000 -g 1000 -f river.hdf5
 fi
 
 # Ensure all nodes can access the HDF5 files

From 8cddb690f5a7f604f34b8e369a4f2b6890987311 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 6 May 2025 18:35:59 +0100
Subject: [PATCH 19/43] Separate install scripts for SWIFT locally and on Cosma

---
 install_swift.sh       | 53 +++++---------------------
 install_swift_cosma.sh | 85 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 95 insertions(+), 43 deletions(-)
 create mode 100755 install_swift_cosma.sh

diff --git a/install_swift.sh b/install_swift.sh
index 94c4491ab..54baf1309 100755
--- a/install_swift.sh
+++ b/install_swift.sh
@@ -1,38 +1,9 @@
 #!/bin/bash
 
-#SBATCH --nodes=1                  # Request 1 node
-#SBATCH --ntasks=1                # Single task for compilation
-#SBATCH --cpus-per-task=16        # Use all cores for parallel make
-#SBATCH --mem=64G                 # Memory for compilation
-#SBATCH -J sw-build              # Job name
-#SBATCH -o sw.out               # Output file
-#SBATCH -e sw.err               # Error file
-#SBATCH -p cosma5               # COSMA5 partition
-#SBATCH -A durham               # Account
-#SBATCH -t 0:30:00             # Increased time for compilation
-
-source ~/swift-env/bin/activate
-
 # Adapt the following lines to your system
-#LOCAL_LIBRARY_PATH=/home/dmitry/local/lib
-#METIS_PATH=/usr/lib/x86_64-linux-gnu
-#PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
-
-module purge
-module load intel_comp/2025.0.1 #intel_comp/2024.2.0
-module load umf compiler-rt tbb compiler mpi
-# module load openmpi/5.0.3
-module load python
-module load fftw/3.3.10
-module load gsl
-module load parmetis/4.0.3-64bit
-module load parallel_hdf5/1.14.4
-module load sundials/5.8.0_c8_single
-module load ffmpeg
-# module load likwid/5.4.1
-
-# Set number of threads for make
-export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
+# LOCAL_LIBRARY_PATH=/home/dmitry/local/lib
+METIS_PATH=/usr/lib/x86_64-linux-gnu
+PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
 
 # The main installation script for SWIFT_ABM
 
@@ -44,30 +15,26 @@ make clean
 echo "#############################"
 echo "# Running './configure' ... #"
 echo "#############################"
-# /configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
-#export CFLAGS="-fsanitize=address -g"
-./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
-		CFLAGS="-Wno-error=gnu-folding-constant" \
-	    LDFLAGS=\
-		--enable-debug \
+# export CFLAGS="-g -pg" #-fsanitize=address 
+./configure CC=gcc CFLAGS="-g -pg" LDFLAGS="-pg -lm" \
 	    --enable-mpi \
 	    --enable-parallel-hdf5 \
-		--with-tbbmalloc \
-	    --with-parmetis \
+	    --with-metis=${METIS_PATH} \
+	    --with-parmetis=${PARMETIS_PATH} \
 	    --with-hydro=abm \
 	    --with-hydro-dimension=2 \
 		--with-abm=human-mobility \
 		--with-ext-potential=human-mobility \
 	    --with-hm=all
 
-		# --enable-ipo \
+	    # LDFLAGS="-pg -L${LOCAL_LIBRARY_PATH}" \
 	    # --with-hm=random-walk
 
 
 echo "########################################"
 echo "# Running 'make -j\$(nproc --all)' ... #"
 echo "########################################"
-make
+make -j$(nproc --all)
 
 echo "#####################################"
 echo "# Running 'ranlib' on libraries ... #"
@@ -82,4 +49,4 @@ make
 echo "###################################"
 echo "# Running 'sudo make install' ... #"
 echo "###################################"
-make install
+sudo make install
diff --git a/install_swift_cosma.sh b/install_swift_cosma.sh
new file mode 100755
index 000000000..94c4491ab
--- /dev/null
+++ b/install_swift_cosma.sh
@@ -0,0 +1,85 @@
+#!/bin/bash
+
+#SBATCH --nodes=1                  # Request 1 node
+#SBATCH --ntasks=1                # Single task for compilation
+#SBATCH --cpus-per-task=16        # Use all cores for parallel make
+#SBATCH --mem=64G                 # Memory for compilation
+#SBATCH -J sw-build              # Job name
+#SBATCH -o sw.out               # Output file
+#SBATCH -e sw.err               # Error file
+#SBATCH -p cosma5               # COSMA5 partition
+#SBATCH -A durham               # Account
+#SBATCH -t 0:30:00             # Increased time for compilation
+
+source ~/swift-env/bin/activate
+
+# Adapt the following lines to your system
+#LOCAL_LIBRARY_PATH=/home/dmitry/local/lib
+#METIS_PATH=/usr/lib/x86_64-linux-gnu
+#PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
+
+module purge
+module load intel_comp/2025.0.1 #intel_comp/2024.2.0
+module load umf compiler-rt tbb compiler mpi
+# module load openmpi/5.0.3
+module load python
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+module load ffmpeg
+# module load likwid/5.4.1
+
+# Set number of threads for make
+export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
+
+# The main installation script for SWIFT_ABM
+
+echo "############################"
+echo "# Running 'make clean' ... #"
+echo "############################"
+make clean
+
+echo "#############################"
+echo "# Running './configure' ... #"
+echo "#############################"
+# /configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
+#export CFLAGS="-fsanitize=address -g"
+./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
+		CFLAGS="-Wno-error=gnu-folding-constant" \
+	    LDFLAGS=\
+		--enable-debug \
+	    --enable-mpi \
+	    --enable-parallel-hdf5 \
+		--with-tbbmalloc \
+	    --with-parmetis \
+	    --with-hydro=abm \
+	    --with-hydro-dimension=2 \
+		--with-abm=human-mobility \
+		--with-ext-potential=human-mobility \
+	    --with-hm=all
+
+		# --enable-ipo \
+	    # --with-hm=random-walk
+
+
+echo "########################################"
+echo "# Running 'make -j\$(nproc --all)' ... #"
+echo "########################################"
+make
+
+echo "#####################################"
+echo "# Running 'ranlib' on libraries ... #"
+echo "#####################################"
+ranlib src/.libs/libswiftsim.a argparse/.libs/libargparse.a src/.libs/libswiftsim_mpi.a
+
+echo "############################"
+echo "# Running 'make' again ... #"
+echo "############################"
+make
+
+echo "###################################"
+echo "# Running 'sudo make install' ... #"
+echo "###################################"
+make install

From 5ca988474b8a650079887132e8f636f5f62772f7 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 6 May 2025 19:09:50 +0100
Subject: [PATCH 20/43] Automate job scheduling more

---
 examples/HumanMobility/job.sh |  4 ++--
 examples/HumanMobility/run.sh | 15 +++++++++++----
 2 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index a94e7e6f2..ca88ae6e3 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -1,8 +1,8 @@
 #!/bin/bash
 
-#SBATCH --ntasks=16             # Total number of MPI tasks (cores) (max 32)
+#SBATCH --ntasks=8             # Total number of MPI tasks (cores) (max 32)
 #SBATCH --nodes=1               # Number of nodes
-#SBATCH --ntasks-per-node=16    # MPI tasks per node (max 32)
+#SBATCH --ntasks-per-node=8    # MPI tasks per node (max 32)
 #SBATCH --cpus-per-task=16      # CPU cores per MPI rank
 #SBATCH --mem=32G               # Memory per node
 #SBATCH -p cosma5               # COSMA5 partition
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 5f84f7f19..8ae670ba9 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -24,12 +24,19 @@ fi
 # Ensure all nodes can access the HDF5 files
 #sync
 #sleep 2  # Give filesystem time to sync
-
 # ulimit -s unlimited
 
-# Run SWIFT
+# Run SWIFT with SLURM environment variables
+# SLURM_NTASKS = total number of MPI tasks
+# SLURM_CPUS_PER_TASK = number of threads per MPI task
+mpirun -np $SLURM_NTASKS swift_mpi --threads=$SLURM_CPUS_PER_TASK \
+    -A -s -g -G \
+    --hm-river \
+    --hm-randomwalk \
+    -n 10000 \
+    humanMobility.yml
+
 # gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
 # swift --hm-river --hm-randomwalk --threads=8 -n 1000 humanMobility.yml # -A -s -g -G 
-mpirun -np 8 swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 10000 humanMobility.yml #--bind-to none 
 # likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml # -A -s -g -G
-# likwid-perfctr -a
\ No newline at end of file
+# likwid-perfctr -a

From b24ee9e717527491c6ea8d9e74f37f935df74048 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 12 May 2025 23:24:28 +0100
Subject: [PATCH 21/43] Enable likwid for performance analysis on Cosma

---
 examples/HumanMobility/humanMobility.yml      |  6 +-
 .../HumanMobility/humanMobility_template.yml  | 75 +++++++++++++++++++
 examples/HumanMobility/job.sh                 | 21 ++++--
 examples/HumanMobility/run.sh                 | 57 ++++++++------
 install_swift_cosma.sh                        | 29 +++----
 5 files changed, 142 insertions(+), 46 deletions(-)
 create mode 100644 examples/HumanMobility/humanMobility_template.yml

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index fa63b2df9..c8e6a076e 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -15,7 +15,7 @@ TimeIntegration:
 
 # Parameters governing the snapshots
 Snapshots:
-  subdir:              data
+  subdir:              data-3
   basename:            humanMobility # Common part of the name of output files
   time_first:          0.         # Time of the first output (in internal units)
   delta_time:          1.0 #1e-2 #1440        # Time difference between consecutive outputs (in internal units)
@@ -36,7 +36,7 @@ Scheduler:
 
 # Parameters related to the initial conditions
 InitialConditions:
-  file_name:  humans.hdf5     # The file to read
+  file_name:  humans-3.hdf5     # The file to read
   periodic:   1
 
 PhysicalConstants:
@@ -72,4 +72,4 @@ Gravity:
 RiverPotential:
 #  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
 #  mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  river.hdf5 # Path to acceleration field HDF5 file
+  parameter_file:  river-3.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/humanMobility_template.yml b/examples/HumanMobility/humanMobility_template.yml
new file mode 100644
index 000000000..776da6cda
--- /dev/null
+++ b/examples/HumanMobility/humanMobility_template.yml
@@ -0,0 +1,75 @@
+# Define the system of units to use internally. 
+InternalUnitSystem:
+  UnitMass_in_cgs:     1 #e+3          # every human has mass 1
+  UnitLength_in_cgs:   1 #e+3        # 1 meter
+  UnitVelocity_in_cgs: 1 #100        # 1 meter per second
+  UnitCurrent_in_cgs:  1          # Amperes
+  UnitTemp_in_cgs:     1          # Kelvin
+
+# Parameters governing the time integration
+TimeIntegration:
+  time_begin: 0.    # The starting time of the simulation (in internal units).
+  time_end:   10 #43200 # minutes in a month  # The end time of the simulation (in internal units).
+  dt_min:     1e-7     # minute #1e-5  # The minimal time-step size of the simulation (in internal units).
+  dt_max:     1e-1 #1440   # The maximal time-step size of the simulation (in internal units).
+
+# Parameters governing the snapshots
+Snapshots:
+  subdir:              ${DATA}
+  basename:            ${HUMANMOBILITY} # Common part of the name of output files
+  time_first:          0.         # Time of the first output (in internal units)
+  delta_time:          1.0 #1e-2 #1440        # Time difference between consecutive outputs (in internal units)
+
+# Parameters governing the conserved quantities statistics
+Statistics:
+  delta_time:          1e-2 #60 # Time between statistics output
+
+# Parameters for the hydrodynamics scheme
+SPH:
+  resolution_eta:        1.2348   # Target smoothing length in units of the mean inter-particle separation (1.2348 == 48Ngbs with the cubic spline kernel).
+  CFL_condition:         0.1      # Courant-Friedrich-Levy condition for time integration.
+
+Scheduler:
+  max_top_level_cells: 16
+  tasks_per_cell: 64
+  # links_per_tasks: 1024
+
+# Parameters related to the initial conditions
+InitialConditions:
+  file_name:  ${HUMANS}.hdf5     # The file to read
+  periodic:   1
+
+PhysicalConstants:
+  G: 1.0  # Gravitational constant in internal units
+
+# Add these gravity parameters
+Gravity:
+  MAC: adaptive           # Multipole Acceptance Criterion (adaptive/geometric)
+  mesh_side_length: 32     # Number of cells in each dimension of the gravity mesh
+  scale_factor_a: 1.0      # Scale factor for cosmological simulations (1.0 for non-cosmological)
+  theta: 0.7              # Opening angle for the gravity calculation
+  theta_cr: 0.5          # Critical opening angle for adaptive MAC
+  theta_min: 0.0         # Minimum opening angle
+  max_smoothing_iterations: 3  # Maximum number of iterations for force smoothing
+  epsilon_fmm: 0.001     # Accuracy parameter for FMM
+  eta: 0.025             # Constant controlling force accuracy in tree-PM algorithm
+  comoving_DM_softening: 0.0  # Softening length for gravity in internal units
+  max_physical_DM_softening: 0.0  # Max physical softening length
+  comoving_baryon_softening: 1.0  # Softening length for gas particles
+  max_physical_baryon_softening: 1.0  # Max physical softening for gas
+  mesh_uses_cell_grad: 1   # Whether to use cell gradients in mesh gravity
+  periodic: 1             # Use periodic gravity (matches InitialConditions)
+
+# ConstantPotential:
+#   g_cgs:      [0., -98, 0.]  # Earth acceleration along z-axis (cgs units)
+
+# PointMassPotential:
+#   position:         [4950., 4950., 0.]  # Location of the point mass (internal units)
+#   useabspos:                   1  # Use absolute positions (0 for relative to centre)
+#   mass:                      1e14  # Mass of the point (internal units)
+#   # timestep_mult:             0.1  # (Optional) The dimensionless constant C in the time-step condition
+
+RiverPotential:
+#  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
+#  mass:     1e6  # "Mass" of the "river" (internal units)
+  parameter_file:  ${RIVER}.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index ca88ae6e3..dda41b295 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -1,13 +1,13 @@
 #!/bin/bash
 
-#SBATCH --ntasks=8             # Total number of MPI tasks (cores) (max 32)
+#SBATCH --ntasks=4             # Total number of MPI tasks (cores) (max 32)
 #SBATCH --nodes=1               # Number of nodes
-#SBATCH --ntasks-per-node=8    # MPI tasks per node (max 32)
-#SBATCH --cpus-per-task=16      # CPU cores per MPI rank
-#SBATCH --mem=32G               # Memory per node
-#SBATCH -p cosma5               # COSMA5 partition
+#SBATCH --ntasks-per-node=4    # MPI tasks per node (max 32)
+#SBATCH --cpus-per-task=4      # CPU cores per MPI rank
+#SBATCH --mem=120G               # Memory per node
+#SBATCH -p cosma               # COSMA5 partition
 #SBATCH -A durham               # Account
-#SBATCH -t 1:00:00
+#SBATCH -t 1-00:00:00
 #SBATCH --mail-type=END
 #SBATCH --mail-user=lcgk69@durham.ac.uk
 
@@ -31,20 +31,25 @@ esac
 # Common module loading for both modes
 module purge
 module load cosma
-module load intel_comp/2025.0.1 # gnu_comp/14.1.0
-module load umf compiler-rt tbb compiler mpi # openmpi/5.0.3
+module load intel_comp/2025.0.1 # gnu_comp/14.1.0 intel_comp/2024.2.0
+module load umf compiler-rt tbb compiler mpi
+# module load openmpi/5.0.3/
 module load python
 module load fftw/3.3.10
 module load gsl
 module load parmetis/4.0.3-64bit
 module load parallel_hdf5/1.14.4
 module load sundials/5.8.0_c8_single
+module load likwid/5.4.1
+
 
 # Additional module for visualisation
 if [ "$mode" = "--vis" ]; then
     module load ffmpeg
 fi
 
+module list
+
 # Execute the appropriate script
 case $mode in
     --run)
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 8ae670ba9..4a177ab26 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -1,42 +1,57 @@
 #!/bin/bash
 
+# Basenames for this run (without extensions)
+HUMANS=humans-3
+RIVER=river-3
+HUMANMOBILITY=humanMobility
+DATA=data-3
+
 # Remove previously generated data and images to regenerate the new ones
-rm humans.hdf5
-rm river.hdf5
-rm data/*
-rm images/*
+# rm ${HUMANS}.hdf5
+# rm ${RIVER}.hdf5
+# rm data/*
+# rm images/*
 
 # Generate the initial conditions if they are not present.
-if [ ! -e humans.hdf5 ]
+if [ ! -e ${HUMANS}.hdf5 ]
 then
     echo "Generating initial conditions for the human mobility box example..."
-    # python3 makeIC.py -t gas -f humans.hdf5  # -t particles
-    python3 makeIC.py -t gas -n 100 -b 10000 -f humans.hdf5  # -t particles
+    # python3 makeIC.py -t gas -f ${HUMANS}.hdf5  # -t particles
+#     python3 makeIC.py -t gas -n 10000 -b 1000000 -f ${HUMANS}.hdf5  # -t particles
+    python3 makeIC.py -t gas -n 100 -b 10000 -f ${HUMANS}.hdf5  # -t particles
 fi
 
 # Generate acceleration field for river
-if [ ! -e river.hdf5 ]
+if [ ! -e ${RIVER}.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRandomRiver.py -b 10000 -g 1000 -f river.hdf5
+    # python3 makeRandomRiver.py -b 1000000 -g 100000 -f ${RIVER}.hdf5
+    python3 makeRandomRiver.py -b 10000 -g 1000 -f ${RIVER}.hdf5
 fi
 
-# Ensure all nodes can access the HDF5 files
-#sync
-#sleep 2  # Give filesystem time to sync
-# ulimit -s unlimited
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVER
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
 
 # Run SWIFT with SLURM environment variables
-# SLURM_NTASKS = total number of MPI tasks
-# SLURM_CPUS_PER_TASK = number of threads per MPI task
-mpirun -np $SLURM_NTASKS swift_mpi --threads=$SLURM_CPUS_PER_TASK \
+# mpirun -np ${SLURM_NTASKS:-8} swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
+# likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g MEM -- \
+likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g FLOPS_DP -- \
+    swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
     -A -s -g -G \
     --hm-river \
     --hm-randomwalk \
-    -n 10000 \
-    humanMobility.yml
+    -n 1000 \
+    ${HUMANMOBILITY}.yml
+
+# module list
 
-# gdb --args swift -g --threads=4 -n 10000 humanMobility.yml # -A -s
-# swift --hm-river --hm-randomwalk --threads=8 -n 1000 humanMobility.yml # -A -s -g -G 
-# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml # -A -s -g -G
+# gdb --args swift -g --threads=4 -n 10000 ${HUMANMOBILITY}.yml # -A -s
+# swift --hm-river --hm-randomwalk --threads=8 -n 1000 ${HUMANMOBILITY}.yml # -A -s -g -G 
+# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 ${HUMANMOBILITY}.yml # -A -s -g -G
+# likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 10 ${HUMANMOBILITY}.yml # -A -s -g -G
+# likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml # -A -s -g -G
+# mpirun -np 8 swift_mpi -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml
+# swift_intel2025 -h | grep version
+# likwid-mpirun -np 8 -t 16 -omp intel -g MEM -- swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
 # likwid-perfctr -a
diff --git a/install_swift_cosma.sh b/install_swift_cosma.sh
index 94c4491ab..8115e2c6e 100755
--- a/install_swift_cosma.sh
+++ b/install_swift_cosma.sh
@@ -7,7 +7,7 @@
 #SBATCH -J sw-build              # Job name
 #SBATCH -o sw.out               # Output file
 #SBATCH -e sw.err               # Error file
-#SBATCH -p cosma5               # COSMA5 partition
+#SBATCH -p cosma               # COSMA5 partition
 #SBATCH -A durham               # Account
 #SBATCH -t 0:30:00             # Increased time for compilation
 
@@ -29,7 +29,7 @@ module load parmetis/4.0.3-64bit
 module load parallel_hdf5/1.14.4
 module load sundials/5.8.0_c8_single
 module load ffmpeg
-# module load likwid/5.4.1
+module load likwid/5.4.1
 
 # Set number of threads for make
 export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
@@ -47,18 +47,19 @@ echo "#############################"
 # /configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
 #export CFLAGS="-fsanitize=address -g"
 ./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
-		CFLAGS="-Wno-error=gnu-folding-constant" \
-	    LDFLAGS=\
-		--enable-debug \
-	    --enable-mpi \
-	    --enable-parallel-hdf5 \
-		--with-tbbmalloc \
-	    --with-parmetis \
-	    --with-hydro=abm \
-	    --with-hydro-dimension=2 \
-		--with-abm=human-mobility \
-		--with-ext-potential=human-mobility \
-	    --with-hm=all
+    --program-suffix=_intel2025 \
+    CFLAGS="-Wno-error=gnu-folding-constant" \
+    LDFLAGS= \
+    --enable-debug \
+    --enable-mpi \
+    --enable-parallel-hdf5 \
+    --with-tbbmalloc \
+    --with-parmetis \
+    --with-hydro=abm \
+    --with-hydro-dimension=2 \
+    --with-abm=human-mobility \
+    --with-ext-potential=human-mobility \
+    --with-hm=all
 
 		# --enable-ipo \
 	    # --with-hm=random-walk

From acd6296a6925739831161e4c357e51107c453a3e Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 13 May 2025 16:40:40 +0100
Subject: [PATCH 22/43] Start performance analysis report

---
 examples/HumanMobility/paws.md | 763 +++++++++++++++++++++++++++++++++
 1 file changed, 763 insertions(+)
 create mode 100644 examples/HumanMobility/paws.md

diff --git a/examples/HumanMobility/paws.md b/examples/HumanMobility/paws.md
new file mode 100644
index 000000000..05ce7ec9a
--- /dev/null
+++ b/examples/HumanMobility/paws.md
@@ -0,0 +1,763 @@
+# Performance analysis
+
+Application: Human Mobility
+
+Parameters:
+
+- Number of people: 10k → 1M humans
+- Area of territory: 100 sq km → 10000 sq km
+
+## Setup
+
+* Cosma
+
+## Benchmarks
+
+### Memory bandwidth (MByte/s) on a single core
+```
+likwid-bench -t triad -W S0:2GB:1
+```
+```
+====
+Starting job 8113868 at Thu  1 May 14:04:59 BST 2025 for user dc-niko3.
+Running on nodes: m5002
+====
+Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
+Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
+Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
+Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
+--------------------------------------------------------------------------------
+LIKWID MICRO BENCHMARK
+Test: triad
+--------------------------------------------------------------------------------
+Using 1 work groups
+Using 1 threads
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+Group: 0 Thread 0 Global Thread 0 running on hwthread 91 - Vector length 62500000 Offset 0
+--------------------------------------------------------------------------------
+Cycles:                 4158690300
+CPU Clock:              2249973589
+Cycle Clock:            2249973589
+Time:                   1.848328e+00 sec
+Iterations:             32
+Iterations per thread:  32
+Inner loop executions:  15625000
+Size (Byte):            2000000000
+Size per thread:        2000000000
+Number of Flops:        4000000000
+MFlops/s:               2164.12
+Data volume (Byte):     64000000000
+MByte/s:                34625.88
+Cycles per update:      2.079345
+Cycles per cacheline:   16.634761
+Loads per update:       3
+Stores per update:      1
+Load bytes per element: 24
+Store bytes per elem.:  8
+Load/store ratio:       3.00
+Instructions:           9500000016
+UOPs:                   15000000000
+--------------------------------------------------------------------------------
+```
+
+### Memory bandwidth (MByte/s) on a full node
+```
+likwid-bench -t triad -W N:2GB:128
+```
+```
+====
+Starting job 8113873 at Thu  1 May 14:08:55 BST 2025 for user dc-niko3.
+Running on nodes: m5002
+====
+Warning: Sanitizing vector length to a multiple of the loop stride 4 and thread count 128 from 62500000 elements (500000000 bytes) to 62499840 elements (499998720 bytes)
+Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
+Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
+Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
+Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
+Initialization: Each thread in domain initializes its own stream chunks
+--------------------------------------------------------------------------------
+LIKWID MICRO BENCHMARK
+Test: triad
+--------------------------------------------------------------------------------
+Using 1 work groups
+Using 128 threads
+--------------------------------------------------------------------------------
+...
+--------------------------------------------------------------------------------
+Cycles:                 438323588617
+CPU Clock:              2249977234
+Cycle Clock:            2249977234
+Time:                   1.948125e+02 sec
+Iterations:             524288
+Iterations per thread:  4096
+Inner loop executions:  122070
+Size (Byte):            1999994880
+Size per thread:        15624960
+Number of Flops:        511998689280
+MFlops/s:               2628.16
+Data volume (Byte):     8191979028480
+MByte/s:                42050.59
+Cycles per update:      1.712206
+Cycles per cacheline:   13.697647
+Loads per update:       3
+Stores per update:      1
+Load bytes per element: 24
+Store bytes per elem.:  8
+Load/store ratio:       3.00
+Instructions:           1215996887056
+UOPs:                   1919995084800
+--------------------------------------------------------------------------------
+```
+
+### Peak floating-point performance (FLOPS) on single core
+```
+likwid-bench -t peakflops -w S0:10000MB:1
+```
+```
+====
+Starting job 8113854 at Thu  1 May 14:01:16 BST 2025 for user dc-niko3.
+Running on nodes: m5002
+====
+Allocate: Process running on hwthread 91 (Domain S0) - Vector length 1250000000/10000000000 Offset 0 Alignment 512
+--------------------------------------------------------------------------------
+LIKWID MICRO BENCHMARK
+Test: peakflops
+--------------------------------------------------------------------------------
+Using 1 work groups
+Using 1 threads
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+Group: 0 Thread 0 Global Thread 0 running on hwthread 91 - Vector length 1250000000 Offset 0
+--------------------------------------------------------------------------------
+Cycles:                 55109423925
+CPU Clock:              2249976828
+Cycle Clock:            2249976828
+Time:                   2.449333e+01 sec
+Iterations:             10
+Iterations per thread:  10
+Inner loop executions:  1250000000
+Size (Byte):            10000000000
+Size per thread:        10000000000
+Number of Flops:        200000000000
+MFlops/s:               8165.49
+Data volume (Byte):     100000000000
+MByte/s:                4082.74
+Cycles per update:      4.408754
+Cycles per cacheline:   35.270031
+Loads per update:       1
+Stores per update:      0
+Load bytes per element: 8
+Store bytes per elem.:  0
+Instructions:           250000000032
+UOPs:                   237500000000
+--------------------------------------------------------------------------------
+```
+
+## Core (caches)
+
+### Serial
+
+#### Memory performance (`--enable-ipo`)
+
+#### Memory performance (`--enable-debug`)
+
+##### 100 x 100 humans on 10 x 10 km for minimum(1000 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=1 -n 1000 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: MEM
++-----------------------+----------+--------------+
+|         Event         |  Counter |  HWThread 0  |
++-----------------------+----------+--------------+
+|   INSTR_RETIRED_ANY   |   FIXC0  | 123986843446 |
+| CPU_CLK_UNHALTED_CORE |   FIXC1  |  70409818611 |
+|  CPU_CLK_UNHALTED_REF |   FIXC2  |  37073751360 |
+|     TOPDOWN_SLOTS     |   FIXC3  | 422458260954 |
+|      CAS_COUNT_RD     |  MBOX0C0 |      2104365 |
+|      CAS_COUNT_WR     |  MBOX0C1 |      1941294 |
+|      CAS_COUNT_RD     |  MBOX1C0 |      2034649 |
+|      CAS_COUNT_WR     |  MBOX1C1 |      1885696 |
+|      CAS_COUNT_RD     |  MBOX2C0 |      1971302 |
+|      CAS_COUNT_WR     |  MBOX2C1 |      1827391 |
+|      CAS_COUNT_RD     |  MBOX3C0 |      2004834 |
+|      CAS_COUNT_WR     |  MBOX3C1 |      1856898 |
+|      CAS_COUNT_RD     |  MBOX4C0 |      2103657 |
+|      CAS_COUNT_WR     |  MBOX4C1 |      1938628 |
+|      CAS_COUNT_RD     |  MBOX5C0 |      2029864 |
+|      CAS_COUNT_WR     |  MBOX5C1 |      1879532 |
+|      CAS_COUNT_RD     |  MBOX6C0 |      1974026 |
+|      CAS_COUNT_WR     |  MBOX6C1 |      1830926 |
+|      CAS_COUNT_RD     |  MBOX7C0 |      2019975 |
+|      CAS_COUNT_WR     |  MBOX7C1 |      1867911 |
+|      CAS_COUNT_RD     |  MBOX8C0 |       -      |
+|      CAS_COUNT_WR     |  MBOX8C1 |       -      |
+|      CAS_COUNT_RD     |  MBOX9C0 |       -      |
+|      CAS_COUNT_WR     |  MBOX9C1 |       -      |
+|      CAS_COUNT_RD     | MBOX10C0 |       -      |
+|      CAS_COUNT_WR     | MBOX10C1 |       -      |
+|      CAS_COUNT_RD     | MBOX11C0 |       -      |
+|      CAS_COUNT_WR     | MBOX11C1 |       -      |
++-----------------------+----------+--------------+
+
++-----------------------------------+------------+
+|               Metric              | HWThread 0 |
++-----------------------------------+------------+
+|        Runtime (RDTSC) [s]        |    19.4184 |
+|        Runtime unhalted [s]       |    35.2053 |
+|            Clock [MHz]            |  3798.3176 |
+|                CPI                |     0.5679 |
+|  Memory read bandwidth [MBytes/s] |    53.5334 |
+|  Memory read data volume [GBytes] |     1.0395 |
+| Memory write bandwidth [MBytes/s] |    49.5309 |
+| Memory write data volume [GBytes] |     0.9618 |
+|    Memory bandwidth [MBytes/s]    |   103.0643 |
+|    Memory data volume [GBytes]    |     2.0013 |
++-----------------------------------+------------+
+```
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum(100 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
+
+```
+```
+--------------------------------------------------------------------------------                                                 
+Group 1: MEM                                                                              
++-----------------------+----------+----------------
+|         Event         |  Counter |   HWThread 0   |
++-----------------------+----------+----------------+
+|   INSTR_RETIRED_ANY   |   FIXC0  | 10078700900771 |
+| CPU_CLK_UNHALTED_CORE |   FIXC1  |  4651632179068 |
+|  CPU_CLK_UNHALTED_REF |   FIXC2  |  2450068924800 |
+|     TOPDOWN_SLOTS     |   FIXC3  | 27909716623362 |
+|      CAS_COUNT_RD     |  MBOX0C0 |      296253668 |
+|      CAS_COUNT_WR     |  MBOX0C1 |      198353712 |
+|      CAS_COUNT_RD     |  MBOX1C0 |      295934061 |
+|      CAS_COUNT_WR     |  MBOX1C1 |      198108469 |
+|      CAS_COUNT_RD     |  MBOX2C0 |      296181293 |
+|      CAS_COUNT_WR     |  MBOX2C1 |      198068319 |
+|      CAS_COUNT_RD     |  MBOX3C0 |      296381234 |
+|      CAS_COUNT_WR     |  MBOX3C1 |      198231128 |
+|      CAS_COUNT_RD     |  MBOX4C0 |      296422014 |
+|      CAS_COUNT_WR     |  MBOX4C1 |      198429254 |
+|      CAS_COUNT_RD     |  MBOX5C0 |      296071633 |
+|      CAS_COUNT_WR     |  MBOX5C1 |      198181758 |
+|      CAS_COUNT_RD     |  MBOX6C0 |      296372650 |
+|      CAS_COUNT_WR     |  MBOX6C1 |      198253434 |
+|      CAS_COUNT_RD     |  MBOX7C0 |      296656051 |
+|      CAS_COUNT_WR     |  MBOX7C1 |      198468244 |
+|      CAS_COUNT_RD     |  MBOX8C0 |        -       |
+|      CAS_COUNT_WR     |  MBOX8C1 |        -       |
+|      CAS_COUNT_RD     |  MBOX9C0 |        -       |
+|      CAS_COUNT_WR     |  MBOX9C1 |        -       |
+|      CAS_COUNT_RD     | MBOX10C0 |        -       |
+|      CAS_COUNT_WR     | MBOX10C1 |        -       |
+|      CAS_COUNT_RD     | MBOX11C0 |        -       |
+|      CAS_COUNT_WR     | MBOX11C1 |        -       |
++-----------------------+----------+----------------+
+
++-----------------------------------+------------+
+|               Metric              | HWThread 0 |
++-----------------------------------+------------+
+|        Runtime (RDTSC) [s]        |  1229.3598 |
+|        Runtime unhalted [s]       |  2325.8498 |
+|            Clock [MHz]            |  3797.0889 |
+|                CPI                |     0.4615 |
+|  Memory read bandwidth [MBytes/s] |   123.3955 |
+|  Memory read data volume [GBytes] |   151.6974 |
+| Memory write bandwidth [MBytes/s] |    82.5715 |
+| Memory write data volume [GBytes] |   101.5100 |
+|    Memory bandwidth [MBytes/s]    |   205.9670 |
+|    Memory data volume [GBytes]    |   253.2075 |
++-----------------------------------+------------+
+```
+
+#### Floating-point performance (`--enable-debug`)
+
+##### 100 x 100 humans on 10 x 10 km for minimum(1000 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=1 -n 1000 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: FLOPS_DP
++------------------------------------------+---------+--------------+
+|                   Event                  | Counter |  HWThread 0  |
++------------------------------------------+---------+--------------+
+|             INSTR_RETIRED_ANY            |  FIXC0  | 143861628268 |
+|           CPU_CLK_UNHALTED_CORE          |  FIXC1  |  79237524584 |
+|           CPU_CLK_UNHALTED_REF           |  FIXC2  |  41722922320 |
+|               TOPDOWN_SLOTS              |  FIXC3  | 475423666740 |
+| FP_ARITH_INST_RETIRED_128B_PACKED_DOUBLE |   PMC0  |    418829623 |
+|    FP_ARITH_INST_RETIRED_SCALAR_DOUBLE   |   PMC1  |   1872322584 |
+| FP_ARITH_INST_RETIRED_256B_PACKED_DOUBLE |   PMC2  |    295344149 |
+| FP_ARITH_INST_RETIRED_512B_PACKED_DOUBLE |   PMC3  |            0 |
++------------------------------------------+---------+--------------+
+
++----------------------+------------+
+|        Metric        | HWThread 0 |
++----------------------+------------+
+|  Runtime (RDTSC) [s] |    21.9525 |
+| Runtime unhalted [s] |    39.6194 |
+|      Clock [MHz]     |  3798.2077 |
+|          CPI         |     0.5508 |
+|     DP [MFLOP/s]     |   177.2623 |
+|   AVX DP [MFLOP/s]   |    53.8150 |
+|  AVX512 DP [MFLOP/s] |          0 |
+|   Packed [MUOPS/s]   |    32.5326 |
+|   Scalar [MUOPS/s]   |    85.2895 |
+|  Vectorization ratio |    27.6116 |
++----------------------+------------+
+```
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum(100 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml # -A -s -g -G
+```
+```
+--------------------------------------------------------------------------------
+Group 1: FLOPS_DP
++------------------------------------------+---------+----------------+
+|                   Event                  | Counter |   HWThread 0   |
++------------------------------------------+---------+----------------+
+|             INSTR_RETIRED_ANY            |  FIXC0  |  9763512748911 |
+|           CPU_CLK_UNHALTED_CORE          |  FIXC1  |  4519527714286 |
+|           CPU_CLK_UNHALTED_REF           |  FIXC2  |  2380559070800 |
+|               TOPDOWN_SLOTS              |  FIXC3  | 27117099211224 |
+| FP_ARITH_INST_RETIRED_128B_PACKED_DOUBLE |   PMC0  |    12350095127 |
+|    FP_ARITH_INST_RETIRED_SCALAR_DOUBLE   |   PMC1  |    13593594077 |
+| FP_ARITH_INST_RETIRED_256B_PACKED_DOUBLE |   PMC2  |    10064623960 |
+| FP_ARITH_INST_RETIRED_512B_PACKED_DOUBLE |   PMC3  |              0 |
++------------------------------------------+---------+----------------+
+
++----------------------+------------+
+|        Metric        | HWThread 0 |
++----------------------+------------+
+|  Runtime (RDTSC) [s] |  1195.7233 |
+| Runtime unhalted [s] |  2259.8068 |
+|      Clock [MHz]     |  3796.9584 |
+|          CPI         |     0.4629 |
+|     DP [MFLOP/s]     |    65.6944 |
+|   AVX DP [MFLOP/s]   |    33.6687 |
+|  AVX512 DP [MFLOP/s] |          0 |
+|   Packed [MUOPS/s]   |    18.7457 |
+|   Scalar [MUOPS/s]   |    11.3685 |
+|  Vectorization ratio |    62.2487 |
++----------------------+------------+
+```
+
+##### Comparison wrt scaling and with benchmarks
+
+---
+
+1. **Scaling: 100x100 Humans (10km x 10km) vs 1000x1000 Humans (100km x 100km)**
+
+**Memory Metrics** (`MEM` group)
+
+| Metric                        | 100x100 Humans | 1000x1000 Humans | Change                |
+|-------------------------------|----------------|------------------|-----------------------|
+| Runtime (RDTSC) [s]           | 19.42          | 1229.36          | ~63.3x increase       |
+| Memory BW [MB/s]              | 103.06         | 205.97           | ~2x increase          |
+| Memory Data Volume [GB]       | 2.00           | 253.21           | ~126.6x increase      |
+| CPI                           | 0.5679         | 0.4615           | ~18.7% better         |
+
+**Floating-Point Metrics** (`FLOPS_DP` group)
+
+| Metric           | 100x100 Humans | 1000x1000 Humans | Change                |
+|------------------|----------------|------------------|-----------------------|
+| DP MFLOP/s       | 177.26         | 65.69            | ~63% decrease         |
+| Vectorization [%]| 27.61          | 62.25            | ~2.25x improvement    |
+| CPI              | 0.5508         | 0.4629           | ~16% improvement      |
+
+---
+
+2. **Comparison with `likwid-bench triad`**
+
+| Metric         | SWIFT (best case) | likwid-bench triad | Ratio      |
+|----------------|------------------|--------------------|------------|
+| Memory BW      | 205.97 MB/s      | 34,625.88 MB/s     | ~0.6%      |
+| Read/Write Ratio | 1.1:1          | 3:1                | Lower      |
+| CPI            | 0.4615           | 2.079              | ~4.5x better |
+
+---
+
+3. **Comparison with `likwid-bench peakflops`**
+
+| Metric         | SWIFT (best case) | likwid-bench peakflops | Ratio      |
+|----------------|------------------|------------------------|------------|
+| MFLOP/s        | 177.26           | 8165.49                | ~2.17%     |
+| Vectorization  | 62.25%           | ~100%                  | ~0.62x     |
+| CPI            | 0.4629           | ~4.41                  | ~9.5x better |
+
+---
+
+**Summary Table**
+
+| Metric                | SWIFT (large, best case) | triad             | peakflops         |
+|-----------------------|-------------------------|-------------------|-------------------|
+| Memory BW [MB/s]      | 205.97                  | 34,625.88         | 4082.74           |
+| DP MFLOP/s            | 65.69                   | (not measured)    | 8165.49           |
+| CPI                   | 0.4615                  | 2.079             | ~4.41             |
+| Vectorization [%]     | 62.25                   | (not measured)    | ~100              |
+
+---
+
+**Key Takeaways**
+
+- **Scaling:** Memory bandwidth and data volume increase with problem size, but bandwidth is still far from hardware peak.
+- **Compared to triad:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
+- **Compared to peakflops:** SWIFT achieves only ~2% of peak FLOPS, and about 5% of peakflops memory bandwidth, with much lower vectorization.
+- **Optimization Potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
+
+---
+
+### Parallel
+
+#### Memory performance (`--enable-debug`, 4 MPI ranks, 4 OMP threads)
+
+##### 100 x 100 humans on 10 x 10 km for minimum(10000 steps, 10 minutes):
+
+```
+likwid-mpirun -np 4 -t 4 -omp intel -g MEM -- swift_mpi --threads=4 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
+```
+```
+Group: 1
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+|         Event         | Counter |   m5228:0:0  |   m5228:0:1  |   m5228:0:2  |   m5228:0:3  |   m5228:1:4  |   m5228:1:5  |   m5228:1:6  |   m5228:1:7  |   m5228:2:8  |   m5228:2:9  |  m5228:2:10  |  m5228:2:11  |  m5228:3:12  |  m5228:3:13  |  m5228:3:14  |  m5228:3:15  |
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+|   INSTR_RETIRED_ANY   |  FIXC0  | 341001383176 |  94476288753 | 108228489509 | 157139864508 | 359764424707 |  92216965479 | 104581778267 | 150788089375 | 371625089799 |  94591782131 | 109177270245 | 154429978651 | 337094080890 |  96098187390 | 110280986563 | 159900454880 |
+| CPU_CLK_UNHALTED_CORE |  FIXC1  | 280895787571 | 108450104598 |  74982138021 | 140211569134 | 288127266919 | 101224781427 |  72250116148 | 132304678131 | 293130918907 | 104687083572 |  73625866015 | 134097259742 | 286173688419 | 109087093895 |  75794547982 | 142937378290 |
+|  CPU_CLK_UNHALTED_REF |  FIXC2  | 234079183520 |  92927087344 |  64614831190 | 120183334544 | 240140408820 |  86879553592 |  62273975608 | 113511073546 | 242431346300 |  90063364742 |  63294626902 | 115109635966 | 236550605798 |  93637848070 |  65134683822 | 122506849556 |
+|      CAS_COUNT_RD     | MBOX0C0 |    395104151 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    391047867 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX0C1 |    323044630 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    301155310 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_RD     | MBOX1C0 |    378297602 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    403500528 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX1C1 |    298478851 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    307279648 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_RD     | MBOX2C0 |    389514168 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    511476132 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX2C1 |    309200166 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    363461718 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_RD     | MBOX3C0 |    372696559 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    463002788 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX3C1 |    296464156 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    306713669 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+
++----------------------------+---------+---------------+-------------+--------------+--------------+
+|            Event           | Counter |      Sum      |     Min     |      Max     |      Avg     |
++----------------------------+---------+---------------+-------------+--------------+--------------+
+|   INSTR_RETIRED_ANY STAT   |  FIXC0  | 2841395114323 | 92216965479 | 371625089799 | 1.775872e+11 |
+| CPU_CLK_UNHALTED_CORE STAT |  FIXC1  | 2417980278771 | 72250116148 | 293130918907 | 1.511238e+11 |
+|  CPU_CLK_UNHALTED_REF STAT |  FIXC2  | 2043338409320 | 62273975608 | 242431346300 | 1.277087e+11 |
+|      CAS_COUNT_RD STAT     | MBOX0C0 |     786152018 |           0 |    395104151 | 4.913450e+07 |
+|      CAS_COUNT_WR STAT     | MBOX0C1 |     624199940 |           0 |    323044630 | 3.901250e+07 |
+|      CAS_COUNT_RD STAT     | MBOX1C0 |     781798130 |           0 |    403500528 | 4.886238e+07 |
+|      CAS_COUNT_WR STAT     | MBOX1C1 |     605758499 |           0 |    307279648 | 3.785991e+07 |
+|      CAS_COUNT_RD STAT     | MBOX2C0 |     900990300 |           0 |    511476132 | 5.631189e+07 |
+|      CAS_COUNT_WR STAT     | MBOX2C1 |     672661884 |           0 |    363461718 | 4.204137e+07 |
+|      CAS_COUNT_RD STAT     | MBOX3C0 |     835699347 |           0 |    463002788 | 5.223121e+07 |
+|      CAS_COUNT_WR STAT     | MBOX3C1 |     603177825 |           0 |    306713669 | 3.769861e+07 |
++----------------------------+---------+---------------+-------------+--------------+--------------+
+
++-----------------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|               Metric              | m5228:0:0 | m5228:0:1 | m5228:0:2 | m5228:0:3 | m5228:1:4 | m5228:1:5 | m5228:1:6 | m5228:1:7 | m5228:2:8 | m5228:2:9 | m5228:2:10 | m5228:2:11 | m5228:3:12 | m5228:3:13 | m5228:3:14 | m5228:3:15 |
++-----------------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|        Runtime (RDTSC) [s]        |  136.6862 |  136.6862 |  136.6862 |  136.6862 |  136.6821 |  136.6821 |  136.6821 |  136.6821 |  136.6847 |  136.6847 |   136.6847 |   136.6847 |   136.6827 |   136.6827 |   136.6827 |   136.6827 |
+|        Runtime unhalted [s]       |  108.0391 |   41.7124 |   28.8399 |   53.9286 |  110.8196 |   38.9331 |   27.7889 |   50.8871 |  112.7437 |   40.2646 |    28.3179 |    51.5763 |   110.0680 |    41.9570 |    29.1521 |    54.9765 |
+|            Clock [MHz]            | 3119.9445 | 3034.2551 | 3017.1023 | 3033.2209 | 3119.5129 | 3029.2627 | 3016.4743 | 3030.4327 | 3143.7093 | 3022.1391 |  3024.3572 |  3028.8498 |  3145.3893 |  3028.9404 |  3025.4810 |  3033.5714 |
+|                CPI                |    0.8237 |    1.1479 |    0.6928 |    0.8923 |    0.8009 |    1.0977 |    0.6908 |    0.8774 |    0.7888 |    1.1067 |     0.6744 |     0.8683 |     0.8489 |     1.1352 |     0.6873 |     0.8939 |
+|  Memory read bandwidth [MBytes/s] |  719.0133 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  828.3133 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+|  Memory read data volume [GBytes] |   98.2792 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  113.2177 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+| Memory write bandwidth [MBytes/s] |  574.6009 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  598.6849 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+| Memory write data volume [GBytes] |   78.5400 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |   81.8311 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+|    Memory bandwidth [MBytes/s]    | 1293.6143 |         0 |         0 |         0 |         0 |         0 |         0 |         0 | 1426.9981 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+|    Memory data volume [GBytes]    |  176.8192 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  195.0488 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
++-----------------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+
++----------------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|                 Metric                 |     Sum    |    Min    |    Max    |    Avg    |  %ile 25  |  %ile 50  |  %ile 75  |
++----------------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|        Runtime (RDTSC) [s] STAT        |  2186.9428 |  136.6821 |  136.6862 |  136.6839 |  136.6821 |  136.6827 |  136.6847 |
+|        Runtime unhalted [s] STAT       |   930.0048 |   27.7889 |  112.7437 |   58.1253 |  112.7437 |   29.1521 |   41.9570 |
+|            Clock [MHz] STAT            | 48852.6429 | 3016.4743 | 3145.3893 | 3053.2902 | 3024.3572 | 3029.2627 | 3034.2551 |
+|                CPI STAT                |    14.0270 |    0.6744 |    1.1479 |    0.8767 |    0.6928 |    0.8489 |    0.8939 |
+|  Memory read bandwidth [MBytes/s] STAT |  1547.3266 |         0 |  828.3133 |   96.7079 |         0 |         0 |         0 |
+|  Memory read data volume [GBytes] STAT |   211.4969 |         0 |  113.2177 |   13.2186 |         0 |         0 |         0 |
+| Memory write bandwidth [MBytes/s] STAT |  1173.2858 |         0 |  598.6849 |   73.3304 |         0 |         0 |         0 |
+| Memory write data volume [GBytes] STAT |   160.3711 |         0 |   81.8311 |   10.0232 |         0 |         0 |         0 |
+|    Memory bandwidth [MBytes/s] STAT    |  2720.6124 |         0 | 1426.9981 |  170.0383 |         0 |         0 |         0 |
+|    Memory data volume [GBytes] STAT    |   371.8680 |         0 |  195.0488 |   23.2417 |         0 |         0 |         0 |
++----------------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+```
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum(1000 steps, 10 minutes):
+
+```
+likwid-mpirun -np 4 -t 4 -omp intel -g MEM -- swift_mpi --threads=4 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
+```
+```
+Group: 1
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+|         Event         | Counter |   m5237:0:0  |   m5237:0:1  |   m5237:0:2  |   m5237:0:3  |   m5237:1:4  |   m5237:1:5  |   m5237:1:6  |   m5237:1:7  |   m5237:2:8  |   m5237:2:9  |  m5237:2:10  |  m5237:2:11  |  m5237:3:12  |  m5237:3:13  |  m5237:3:14  |  m5237:3:15  |
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+|   INSTR_RETIRED_ANY   |  FIXC0  | 935027159945 | 918078869462 | 945972539732 | 957959870956 | 896847519524 | 874744379907 | 924260839133 | 927599423107 | 916968570718 | 890387375529 | 933863665539 | 943080011762 | 903224228888 | 883709778741 | 928253499166 | 933640852265 |
+| CPU_CLK_UNHALTED_CORE |  FIXC1  | 804755146770 | 779172644940 | 781786388839 | 789189897021 | 768855237917 | 744265028808 | 764818386042 | 763329436089 | 787163890777 | 758183098203 | 775728499290 | 775663816515 | 773399863303 | 748645688585 | 767735222132 | 764163093009 |
+|  CPU_CLK_UNHALTED_REF |  FIXC2  | 696778006886 | 675063194728 | 677139994726 | 683414274686 | 665560928110 | 644816364608 | 662459649124 | 661041261192 | 680841627024 | 656554391870 | 671170753370 | 671005159318 | 669071116116 | 648364992652 | 664244851062 | 660990384080 |
+|      CAS_COUNT_RD     | MBOX0C0 |   1303101073 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |   1173952283 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX0C1 |    604088681 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    516028925 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_RD     | MBOX1C0 |   1253823219 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |   1181399098 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX1C1 |    518406987 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    493431066 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_RD     | MBOX2C0 |   1249469599 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |   1279012528 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX2C1 |    547663411 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    570219086 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_RD     | MBOX3C0 |   1251631196 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |   1274434211 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
+|      CAS_COUNT_WR     | MBOX3C1 |    517600181 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    493283369 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+
++----------------------------+---------+----------------+--------------+--------------+--------------+
+|            Event           | Counter |       Sum      |      Min     |      Max     |      Avg     |
++----------------------------+---------+----------------+--------------+--------------+--------------+
+|   INSTR_RETIRED_ANY STAT   |  FIXC0  | 14713618584374 | 874744379907 | 957959870956 | 9.196012e+11 |
+| CPU_CLK_UNHALTED_CORE STAT |  FIXC1  | 12346855338240 | 744265028808 | 804755146770 | 771678458640 |
+|  CPU_CLK_UNHALTED_REF STAT |  FIXC2  | 10688516949552 | 644816364608 | 696778006886 | 668032309347 |
+|      CAS_COUNT_RD STAT     | MBOX0C0 |     2477053356 |            0 |   1303101073 | 1.548158e+08 |
+|      CAS_COUNT_WR STAT     | MBOX0C1 |     1120117606 |            0 |    604088681 | 7.000735e+07 |
+|      CAS_COUNT_RD STAT     | MBOX1C0 |     2435222317 |            0 |   1253823219 | 1.522014e+08 |
+|      CAS_COUNT_WR STAT     | MBOX1C1 |     1011838053 |            0 |    518406987 | 6.323988e+07 |
+|      CAS_COUNT_RD STAT     | MBOX2C0 |     2528482127 |            0 |   1279012528 | 1.580301e+08 |
+|      CAS_COUNT_WR STAT     | MBOX2C1 |     1117882497 |            0 |    570219086 | 6.986766e+07 |
+|      CAS_COUNT_RD STAT     | MBOX3C0 |     2526065407 |            0 |   1274434211 | 1.578791e+08 |
+|      CAS_COUNT_WR STAT     | MBOX3C1 |     1010883550 |            0 |    517600181 | 6.318022e+07 |
++----------------------------+---------+----------------+--------------+--------------+--------------+
+
++-----------------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|               Metric              | m5237:0:0 | m5237:0:1 | m5237:0:2 | m5237:0:3 | m5237:1:4 | m5237:1:5 | m5237:1:6 | m5237:1:7 | m5237:2:8 | m5237:2:9 | m5237:2:10 | m5237:2:11 | m5237:3:12 | m5237:3:13 | m5237:3:14 | m5237:3:15 |
++-----------------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|        Runtime (RDTSC) [s]        |  306.1496 |  306.1496 |  306.1496 |  306.1496 |  306.1241 |  306.1241 |  306.1241 |  306.1241 |  306.1456 |  306.1456 |   306.1456 |   306.1456 |   306.1473 |   306.1473 |   306.1473 |   306.1473 |
+|        Runtime unhalted [s]       |  309.5251 |  299.6855 |  300.6908 |  303.5383 |  295.7213 |  286.2632 |  294.1686 |  293.5959 |  302.7623 |  291.6156 |   298.3640 |   298.3391 |   297.4647 |   287.9437 |   295.2859 |   293.9120 |
+|            Clock [MHz]            | 3002.8752 | 3000.9393 | 3001.7712 | 3002.3784 | 3003.4387 | 3000.9142 | 3001.6559 | 3002.2404 | 3005.9542 | 3002.3871 |  3004.9692 |  3005.4602 |  3005.3877 |  3002.1022 |  3005.0518 |  3005.7968 |
+|                CPI                |    0.8607 |    0.8487 |    0.8264 |    0.8238 |    0.8573 |    0.8508 |    0.8275 |    0.8229 |    0.8584 |    0.8515 |     0.8307 |     0.8225 |     0.8563 |     0.8472 |     0.8271 |     0.8185 |
+|  Memory read bandwidth [MBytes/s] | 1057.3707 |         0 |         0 |         0 |         0 |         0 |         0 |         0 | 1026.1885 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+|  Memory read data volume [GBytes] |  323.7136 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  314.1631 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+| Memory write bandwidth [MBytes/s] |  457.3470 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  433.3546 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+| Memory write data volume [GBytes] |  140.0166 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  132.6696 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+|    Memory bandwidth [MBytes/s]    | 1514.7176 |         0 |         0 |         0 |         0 |         0 |         0 |         0 | 1459.5430 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
+|    Memory data volume [GBytes]    |  463.7302 |         0 |         0 |         0 |         0 |         0 |         0 |         0 |  446.8327 |         0 |          0 |          0 |          0 |          0 |          0 |          0 |
++-----------------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+
++----------------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|                 Metric                 |     Sum    |    Min    |    Max    |    Avg    |  %ile 25  |  %ile 50  |  %ile 75  |
++----------------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|        Runtime (RDTSC) [s] STAT        |  4898.2664 |  306.1241 |  306.1496 |  306.1416 |  306.1241 |  306.1456 |  306.1473 |
+|        Runtime unhalted [s] STAT       |  4748.8760 |  286.2632 |  309.5251 |  296.8048 |  293.5959 |  295.7213 |  299.6855 |
+|            Clock [MHz] STAT            | 48053.3225 | 3000.9142 | 3005.9542 | 3003.3327 | 3001.7712 | 3002.3871 | 3005.0518 |
+|                CPI STAT                |    13.4303 |    0.8185 |    0.8607 |    0.8394 |    0.8238 |    0.8307 |    0.8515 |
+|  Memory read bandwidth [MBytes/s] STAT |  2083.5592 |         0 | 1057.3707 |  130.2224 |         0 |         0 |         0 |
+|  Memory read data volume [GBytes] STAT |   637.8767 |         0 |  323.7136 |   39.8673 |         0 |         0 |         0 |
+| Memory write bandwidth [MBytes/s] STAT |   890.7016 |         0 |  457.3470 |   55.6688 |         0 |         0 |         0 |
+| Memory write data volume [GBytes] STAT |   272.6862 |         0 |  140.0166 |   17.0429 |         0 |         0 |         0 |
+|    Memory bandwidth [MBytes/s] STAT    |  2974.2606 |         0 | 1514.7176 |  185.8913 |         0 |         0 |         0 |
+|    Memory data volume [GBytes] STAT    |   910.5629 |         0 |  463.7302 |   56.9102 |         0 |         0 |         0 |
++----------------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+```
+
+#### Floating-point performance (`--enable-debug`)
+
+##### 100 x 100 humans on 10 x 10 km for minimum(10000 steps, 10 minutes):
+
+```
+likwid-mpirun -np 4 -t 4 -omp intel -g FLOPS_DP -- swift_mpi --threads=4 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
+```
+```
+Group: 1
++--------------------------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+-------------+--------------+--------------+--------------+-------------+--------------+--------------+
+|                 Event                | Counter |   m5335:0:0  |   m5335:0:1  |   m5335:0:2  |   m5335:0:3  |   m5335:1:4  |   m5335:1:5  |   m5335:1:6  |   m5335:1:7  |   m5335:2:8  |  m5335:2:9  |  m5335:2:10  |  m5335:2:11  |  m5335:3:12  |  m5335:3:13 |  m5335:3:14  |  m5335:3:15  |
++--------------------------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+-------------+--------------+--------------+--------------+-------------+--------------+--------------+
+|           INSTR_RETIRED_ANY          |  FIXC0  | 321639846446 |  95295603041 | 108910848024 | 155472825044 | 313546724897 |  94256234895 | 108124055942 | 153092993255 | 354102823655 | 90172462296 | 103862094786 | 147648439350 | 348594216141 | 89095844083 | 102221345209 | 147222018909 |
+|         CPU_CLK_UNHALTED_CORE        |  FIXC1  | 270939072666 | 108795059797 |  73441745166 | 136871060681 | 270188518834 | 106475416429 |  74103623128 | 136847769255 | 279981732694 | 99721523895 |  71746250642 | 129586517363 | 279131669583 | 98854040792 |  70657886881 | 128905664172 |
+|         CPU_CLK_UNHALTED_REF         |  FIXC2  | 226196112298 |  93182929476 |  63301619290 | 117267745842 | 225514052218 |  91209295294 |  63836360484 | 117253940180 | 231285837224 | 85652307312 |  61703251714 | 111148431316 | 230557302768 | 84904931202 |  60771101846 | 110549719124 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE |   PMC0  |   1233656017 |    299866399 |    207790973 |    267129364 |   1436475677 |    283846907 |    192877546 |    260322176 |    849148674 |   293379462 |    203124677 |    267482063 |    754057509 |   286105969 |    196003709 |    258701926 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE |   PMC1  |   4928924598 |   1450613650 |    605275577 |   1301031710 |   5716699045 |   1423932221 |    568589565 |   1313471659 |   3411173582 |  1341345940 |    586246720 |   1242421796 |   3063556224 |  1343623328 |    567353486 |   1228806835 |
+|       SIMD_FP_256_PACKED_DOUBLE      |   PMC2  |    210446767 |    197850878 |    113108074 |    140181433 |    196810155 |    187607559 |    104398736 |    134087686 |    205509225 |   192591120 |    107716587 |    140119426 |    197362833 |   186098897 |    102038631 |    132977922 |
++--------------------------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+-------------+--------------+--------------+--------------+-------------+--------------+--------------+
+
++-------------------------------------------+---------+---------------+-------------+--------------+--------------+
+|                   Event                   | Counter |      Sum      |     Min     |      Max     |      Avg     |
++-------------------------------------------+---------+---------------+-------------+--------------+--------------+
+|           INSTR_RETIRED_ANY STAT          |  FIXC0  | 2733258375973 | 89095844083 | 354102823655 | 1.708286e+11 |
+|         CPU_CLK_UNHALTED_CORE STAT        |  FIXC1  | 2336247551978 | 70657886881 | 279981732694 | 1.460155e+11 |
+|         CPU_CLK_UNHALTED_REF STAT         |  FIXC2  | 1974334937588 | 60771101846 | 231285837224 | 1.233959e+11 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE STAT |   PMC0  |    7289969048 |   192877546 |   1436475677 | 4.556231e+08 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE STAT |   PMC1  |   30093065936 |   567353486 |   5716699045 |   1880816621 |
+|       SIMD_FP_256_PACKED_DOUBLE STAT      |   PMC2  |    2548905929 |   102038631 |    210446767 | 1.593066e+08 |
++-------------------------------------------+---------+---------------+-------------+--------------+--------------+
+
++-------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|          Metric         | m5335:0:0 | m5335:0:1 | m5335:0:2 | m5335:0:3 | m5335:1:4 | m5335:1:5 | m5335:1:6 | m5335:1:7 | m5335:2:8 | m5335:2:9 | m5335:2:10 | m5335:2:11 | m5335:3:12 | m5335:3:13 | m5335:3:14 | m5335:3:15 |
++-------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|   Runtime (RDTSC) [s]   |  132.3468 |  132.3468 |  132.3468 |  132.3468 |  132.3353 |  132.3353 |  132.3353 |  132.3353 |  132.3355 |  132.3355 |   132.3355 |   132.3355 |   132.3330 |   132.3330 |   132.3330 |   132.3330 |
+|   Runtime unhalted [s]  |  104.2074 |   41.8443 |   28.2469 |   52.6428 |  103.9211 |   40.9530 |   28.5021 |   52.6350 |  107.6876 |   38.3553 |    27.5953 |    49.8421 |   107.3587 |    38.0209 |    27.1762 |    49.5793 |
+|       Clock [MHz]       | 3114.2927 | 3035.6084 | 3016.4845 | 3034.6317 | 3114.9884 | 3035.1028 | 3018.1059 | 3034.4042 | 3147.3464 | 3027.0091 |  3023.1176 |  3031.2403 |  3147.7637 |  3027.1462 |  3022.9810 |  3031.7001 |
+|           CPI           |    0.8424 |    1.1417 |    0.6743 |    0.8804 |    0.8617 |    1.1296 |    0.6854 |    0.8939 |    0.7907 |    1.1059 |     0.6908 |     0.8777 |     0.8007 |     1.1095 |     0.6912 |     0.8756 |
+|       DP [MFLOP/s]      |   62.2457 |   21.4720 |   11.1320 |   18.1041 |   70.8571 |   20.7205 |   10.3671 |   17.9126 |   44.8217 |   20.3911 |    10.7557 |    17.6662 |    40.5124 |    20.1026 |    10.3339 |    17.2151 |
+|     AVX DP [MFLOP/s]    |    6.3605 |    5.9798 |    3.4185 |    4.2368 |    5.9488 |    5.6707 |    3.1556 |    4.0530 |    6.2118 |    5.8213 |     3.2559 |     4.2353 |     5.9656 |     5.6252 |     3.0843 |     4.0195 |
+|     Packed [MUOPS/s]    |   10.9115 |    3.7607 |    2.4247 |    3.0776 |   12.3420 |    3.5626 |    2.2464 |    2.9804 |    7.9696 |    3.6723 |     2.3489 |     3.0801 |     7.1896 |     3.5683 |     2.2522 |     2.9598 |
+|     Scalar [MUOPS/s]    |   37.2425 |   10.9607 |    4.5734 |    9.8305 |   43.1986 |   10.7600 |    4.2966 |    9.9253 |   25.7767 |   10.1359 |     4.4300 |     9.3884 |    23.1504 |    10.1534 |     4.2873 |     9.2857 |
+| Vectorization ratio [%] |   22.6596 |   25.5458 |   34.6478 |   23.8425 |   22.2216 |   24.8738 |   34.3328 |   23.0935 |   23.6162 |   26.5948 |    34.6500 |    24.7028 |    23.6968 |    26.0049 |    34.4400 |    24.1705 |
++-------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+
++------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|            Metric            |     Sum    |    Min    |    Max    |    Avg    |  %ile 25  |  %ile 50  |  %ile 75  |
++------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|   Runtime (RDTSC) [s] STAT   |  2117.4024 |  132.3330 |  132.3468 |  132.3376 |  132.3330 |  132.3353 |  132.3355 |
+|   Runtime unhalted [s] STAT  |   898.5680 |   27.1762 |  107.6876 |   56.1605 |  107.6876 |   28.5021 |   41.8443 |
+|       Clock [MHz] STAT       | 48861.9230 | 3016.4845 | 3147.7637 | 3053.8702 | 3023.1176 | 3031.7001 | 3035.6084 |
+|           CPI STAT           |    14.0515 |    0.6743 |    1.1417 |    0.8782 |    0.6912 |    0.8617 |    0.8939 |
+|       DP [MFLOP/s] STAT      |   414.6098 |   10.3339 |   70.8571 |   25.9131 |   11.1320 |   18.1041 |   21.4720 |
+|     AVX DP [MFLOP/s] STAT    |    77.0426 |    3.0843 |    6.3605 |    4.8152 |    3.4185 |    4.2368 |    5.9488 |
+|     Packed [MUOPS/s] STAT    |    74.3467 |    2.2464 |   12.3420 |    4.6467 |    2.2522 |    2.9804 |    3.5683 |
+|     Scalar [MUOPS/s] STAT    |   227.3954 |    4.2873 |   43.1986 |   14.2122 |   10.9607 |    4.2873 |   43.1986 |
+| Vectorization ratio [%] STAT |   429.0934 |   22.2216 |   34.6500 |   26.8183 |   23.6162 |   24.7028 |   26.5948 |
++------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+```
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum(1000 steps, 10 minutes):
+
+```
+likwid-mpirun -np 4 -t 4 -omp intel -g FLOPS_DP -- swift_mpi --threads=4 -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+```
+```
+Group: 1
++--------------------------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+
+|                 Event                | Counter |   m5334:0:0  |   m5334:0:1  |   m5334:0:2  |   m5334:0:3  |   m5334:1:4  |   m5334:1:5  |   m5334:1:6  |   m5334:1:7  |   m5334:2:8  |   m5334:2:9  |  m5334:2:10  |  m5334:2:11  |  m5334:3:12  |  m5334:3:13  |  m5334:3:14  |   m5334:3:15  |
++--------------------------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+
+|           INSTR_RETIRED_ANY          |  FIXC0  | 928603457884 | 911573592017 | 969853890424 | 975888043593 | 990161259348 | 966007358758 | 980630430015 | 997507812056 | 949733963252 | 928128991523 | 980345658060 | 996101066135 | 964132995603 | 940658959069 | 998496468941 | 1007029168610 |
+|         CPU_CLK_UNHALTED_CORE        |  FIXC1  | 793709132880 | 770955305231 | 799673078449 | 799040042510 | 841996850660 | 816201744613 | 807755583411 | 814456886578 | 810874685654 | 782474264230 | 808541053373 | 813750612603 | 823452300321 | 793851472176 | 812653860819 |  816543101779 |
+|         CPU_CLK_UNHALTED_REF         |  FIXC2  | 687190362924 | 667981690480 | 692733283216 | 692082389804 | 728841882236 | 707198316214 | 699755009798 | 705453081450 | 701540400028 | 677807241384 | 699089757886 | 703420064984 | 712007211786 | 687443295084 | 702251906564 |  705448866694 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE |   PMC0  |   2446903175 |   2539497409 |   2595236120 |   3596634676 |   2543018613 |   2653218612 |   2881304512 |   3667504788 |   2426244399 |   2644511043 |   2551783983 |   3716724177 |   2502062051 |   2633380750 |   2635341656 |    3560837119 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE |   PMC1  |   3392789115 |   3292957442 |   3205400447 |   4053329366 |   3470664633 |   3477100065 |   3488209717 |   4221351979 |   3407043697 |   3406085381 |   3167849851 |   4203688028 |   3415853821 |   3450611329 |   3248905361 |    4075688959 |
+|       SIMD_FP_256_PACKED_DOUBLE      |   PMC2  |   3797006147 |   3891979016 |   3924927923 |   3314325646 |   4333749324 |   4184528274 |   4175025409 |   3810679152 |   4032276636 |   4071116005 |   4021960270 |   3476932297 |   4103566526 |   4129436531 |   4058977364 |    3650775699 |
++--------------------------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+
+
++-------------------------------------------+---------+----------------+--------------+---------------+--------------+
+|                   Event                   | Counter |       Sum      |      Min     |      Max      |      Avg     |
++-------------------------------------------+---------+----------------+--------------+---------------+--------------+
+|           INSTR_RETIRED_ANY STAT          |  FIXC0  | 15484853115288 | 911573592017 | 1007029168610 | 9.678033e+11 |
+|         CPU_CLK_UNHALTED_CORE STAT        |  FIXC1  | 12905929975287 | 770955305231 |  841996850660 | 8.066206e+11 |
+|         CPU_CLK_UNHALTED_REF STAT         |  FIXC2  | 11170244760532 | 667981690480 |  728841882236 | 6.981403e+11 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE STAT |   PMC0  |    45594203083 |   2426244399 |    3716724177 | 2.849638e+09 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE STAT |   PMC1  |    56977529191 |   3167849851 |    4221351979 | 3.561096e+09 |
+|       SIMD_FP_256_PACKED_DOUBLE STAT      |   PMC2  |    62977262219 |   3314325646 |    4333749324 | 3.936079e+09 |
++-------------------------------------------+---------+----------------+--------------+---------------+--------------+
+
++-------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|          Metric         | m5334:0:0 | m5334:0:1 | m5334:0:2 | m5334:0:3 | m5334:1:4 | m5334:1:5 | m5334:1:6 | m5334:1:7 | m5334:2:8 | m5334:2:9 | m5334:2:10 | m5334:2:11 | m5334:3:12 | m5334:3:13 | m5334:3:14 | m5334:3:15 |
++-------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+|   Runtime (RDTSC) [s]   |  323.8998 |  323.8998 |  323.8998 |  323.8998 |  323.9200 |  323.9200 |  323.9200 |  323.9200 |  323.9356 |  323.9356 |   323.9356 |   323.9356 |   323.9037 |   323.9037 |   323.9037 |   323.9037 |
+|   Runtime unhalted [s]  |  305.2741 |  296.5226 |  307.5680 |  307.3245 |  323.8471 |  313.9259 |  310.6773 |  313.2548 |  311.8845 |  300.9610 |   310.9870 |   312.9907 |   316.7138 |   305.3289 |   312.5606 |   314.0564 |
+|       Clock [MHz]       | 3003.0024 | 3000.7928 | 3001.3579 | 3001.8024 | 3003.6377 | 3000.7285 | 3001.2650 | 3001.7213 | 3005.1141 | 3001.3992 |  3006.9696 |  3007.7136 |  3006.9449 |  3002.4362 |  3008.7360 |  3009.4351 |
+|           CPI           |    0.8547 |    0.8457 |    0.8245 |    0.8188 |    0.8504 |    0.8449 |    0.8237 |    0.8165 |    0.8538 |    0.8431 |     0.8248 |     0.8169 |     0.8541 |     0.8439 |     0.8139 |     0.8108 |
+|       DP [MFLOP/s]      |   72.4749 |   73.9113 |   74.3921 |   75.6527 |   79.9324 |   78.7900 |   80.1152 |   82.7336 |   75.2885 |   77.1128 |    75.1978 |    78.8579 |    76.6717 |    77.9093 |    76.4286 |    79.6547 |
+|     AVX DP [MFLOP/s]    |   46.8911 |   48.0640 |   48.4709 |   40.9303 |   53.5163 |   51.6736 |   51.5563 |   47.0570 |   49.7911 |   50.2707 |    49.6637 |    42.9336 |    50.6764 |    50.9959 |    50.1257 |    45.0847 |
+|     Packed [MUOPS/s]    |   19.2773 |   19.8564 |   20.1302 |   21.3367 |   21.2298 |   21.1094 |   21.7842 |   23.0865 |   19.9377 |   20.7314 |    20.2934 |    22.2071 |    20.3938 |    20.8791 |    20.6676 |    22.2647 |
+|     Scalar [MUOPS/s]    |   10.4748 |   10.1666 |    9.8963 |   12.5141 |   10.7146 |   10.7344 |   10.7687 |   13.0321 |   10.5177 |   10.5147 |     9.7793 |    12.9769 |    10.5459 |    10.6532 |    10.0305 |    12.5830 |
+| Vectorization ratio [%] |   64.7930 |   66.1373 |   67.0415 |   63.0315 |   66.4587 |   66.2903 |   66.9193 |   63.9186 |   65.4653 |   66.3487 |    67.4812 |    63.1170 |    65.9147 |    66.2150 |    67.3254 |    63.8914 |
++-------------------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+
+
++------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|            Metric            |     Sum    |    Min    |    Max    |    Avg    |  %ile 25  |  %ile 50  |  %ile 75  |
++------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+|   Runtime (RDTSC) [s] STAT   |  5182.6364 |  323.8998 |  323.9356 |  323.9148 |  323.8998 |  323.9037 |  323.9200 |
+|   Runtime unhalted [s] STAT  |  4963.8772 |  296.5226 |  323.8471 |  310.2423 |  305.3289 |  310.9870 |  313.2548 |
+|       Clock [MHz] STAT       | 48063.0567 | 3000.7285 | 3009.4351 | 3003.9410 | 3001.3579 | 3002.4362 | 3006.9449 |
+|           CPI STAT           |    13.3405 |    0.8108 |    0.8547 |    0.8338 |    0.8169 |    0.8248 |    0.8457 |
+|       DP [MFLOP/s] STAT      |  1235.1235 |   72.4749 |   82.7336 |   77.1952 |   75.1978 |   76.6717 |   78.8579 |
+|     AVX DP [MFLOP/s] STAT    |   777.7013 |   40.9303 |   53.5163 |   48.6063 |   46.8911 |   49.6637 |   50.6764 |
+|     Packed [MUOPS/s] STAT    |   335.1853 |   19.2773 |   23.0865 |   20.9491 |   20.1302 |   20.7314 |   21.3367 |
+|     Scalar [MUOPS/s] STAT    |   175.9028 |    9.7793 |   13.0321 |   10.9939 |   10.5147 |   10.7146 |   12.5830 |
+| Vectorization ratio [%] STAT |  1050.3489 |   63.0315 |   67.4812 |   65.6468 |   63.9186 |   66.1373 |   66.4587 |
++------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
+```
+
+##### Comparison wrt scaling and with benchmarks
+
+---
+
+1. **Scaling**: 100x100 Humans (10km x 10km) vs 1000x1000 Humans (100km x 100km)
+
+**Memory Metrics** (`MEM` group, best rank)
+
+| Metric                        | 100x100 Humans | 1000x1000 Humans | Change                |
+|-------------------------------|----------------|------------------|-----------------------|
+| Runtime (RDTSC) [s]           | 136.69         | 306.15           | ~2.24x increase       |
+| Memory BW [MB/s]              | 1427           | 1515             | Slight increase       |
+| Memory Data Volume [GB]       | 195            | 464              | ~2.38x increase       |
+| CPI                           | 0.82           | 0.84             | Similar               |
+
+**Floating-Point Metrics** (`FLOPS_DP` group, best rank)
+
+| Metric           | 100x100 Humans | 1000x1000 Humans | Change                |
+|------------------|----------------|------------------|-----------------------|
+| DP MFLOP/s       | ~62            | ~44              | ~29% decrease         |
+| Vectorization [%]| ~22–34         | ~22–34           | Similar (low)         |
+| CPI              | ~0.84          | ~0.84            | Similar               |
+
+---
+
+2. **Comparison with `likwid-bench triad`**
+
+| Metric         | swift_mpi (best rank) | likwid-bench triad | Ratio      |
+|----------------|----------------------|--------------------|------------|
+| Memory BW      | 1515 MB/s            | 34,625.88 MB/s     | ~4.4%      |
+| Read/Write Ratio | ~1.8:1             | 3:1                | Lower      |
+| CPI            | 0.84                 | 2.08               | ~2.5x better |
+
+- **Observation:**  
+  - SWIFT achieves only ~4.4% of the streaming memory bandwidth of triad.
+  - Access pattern is less streaming (lower read/write ratio).
+  - CPI is better in SWIFT, but this is expected for memory-bound code.
+
+---
+
+3. **Comparison with `likwid-bench peakflops`**
+
+| Metric         | swift_mpi (best rank) | likwid-bench peakflops | Ratio      |
+|----------------|----------------------|------------------------|------------|
+| MFLOP/s        | ~62                  | 8165.49                | ~0.76%     |
+| Memory BW      | 1515 MB/s            | 4082.74 MB/s           | ~37%       |
+| CPI            | 0.84                 | ~4.41                  | ~5x better |
+| Vectorization  | ~22–34%              | ~100%                  | ~0.22–0.34x|
+
+- **Observation:**  
+  - SWIFT achieves less than 1% of peak FLOPS and about 37% of peakflops memory bandwidth.
+  - Vectorization ratio is much lower than peakflops.
+  - CPI is much better in SWIFT, but peakflops is compute-bound.
+
+---
+
+**Summary Table**
+
+| Metric                | swift_mpi (large, best rank) | triad             | peakflops         |
+|-----------------------|------------------------------|-------------------|-------------------|
+| Memory BW [MB/s]      | 1515                         | 34,625.88         | 4082.74           |
+| DP MFLOP/s            | ~44                          | (not measured)    | 8165.49           |
+| CPI                   | 0.84                         | 2.08              | ~4.41             |
+| Vectorization [%]     | ~22–34                       | (not measured)    | ~100              |
+
+---
+
+**Key Takeaways**
+
+- **Scaling:** Memory bandwidth and data volume increase with problem size, but bandwidth is still far from hardware peak.
+- **Compared to triad:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
+- **Compared to peakflops:** SWIFT achieves less than 1% of peak FLOPS, and about 37% of peakflops memory bandwidth, with much lower vectorization.
+- **Optimization Potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
+
+---
+
+## Intra (shared memory, threads)
+
+## Inter (MPI)
+
+### Strong scaling
+
+### Weak scaling
+
+### Load balancing
\ No newline at end of file

From 04cf0b137fa5ed0d7ed3a863532711cc2fe5162a Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 13 May 2025 16:44:31 +0100
Subject: [PATCH 23/43] Move performance analysis stuff to a subfolder

---
 examples/HumanMobility/{ => perf}/paws.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/HumanMobility/{ => perf}/paws.md (100%)

diff --git a/examples/HumanMobility/paws.md b/examples/HumanMobility/perf/paws.md
similarity index 100%
rename from examples/HumanMobility/paws.md
rename to examples/HumanMobility/perf/paws.md

From f475dc88a5e8d32a506134eca52fbda10ad3eb6b Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 13 May 2025 18:03:04 +0100
Subject: [PATCH 24/43] Update performance analysis (Core)

---
 examples/HumanMobility/perf/paws.md | 248 ++++++++++++++--------------
 1 file changed, 122 insertions(+), 126 deletions(-)

diff --git a/examples/HumanMobility/perf/paws.md b/examples/HumanMobility/perf/paws.md
index 05ce7ec9a..82da5d5c9 100644
--- a/examples/HumanMobility/perf/paws.md
+++ b/examples/HumanMobility/perf/paws.md
@@ -19,132 +19,145 @@ likwid-bench -t triad -W S0:2GB:1
 ```
 ```
 ====
-Starting job 8113868 at Thu  1 May 14:04:59 BST 2025 for user dc-niko3.
-Running on nodes: m5002
-====
-Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
-Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
-Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
-Allocate: Process running on hwthread 91 (Domain S0) - Vector length 62500000/500000000 Offset 0 Alignment 512
---------------------------------------------------------------------------------
+Starting job ... 
+...
 LIKWID MICRO BENCHMARK
 Test: triad
---------------------------------------------------------------------------------
+...
 Using 1 work groups
 Using 1 threads
---------------------------------------------------------------------------------
---------------------------------------------------------------------------------
-Group: 0 Thread 0 Global Thread 0 running on hwthread 91 - Vector length 62500000 Offset 0
---------------------------------------------------------------------------------
-Cycles:                 4158690300
-CPU Clock:              2249973589
-Cycle Clock:            2249973589
-Time:                   1.848328e+00 sec
-Iterations:             32
-Iterations per thread:  32
+...
+Cycles:                 3591308877
+CPU Clock:              2599987191
+Cycle Clock:            2599987191
+Time:                   1.381279e+00 sec
+Iterations:             10
+Iterations per thread:  10
 Inner loop executions:  15625000
 Size (Byte):            2000000000
 Size per thread:        2000000000
-Number of Flops:        4000000000
-MFlops/s:               2164.12
-Data volume (Byte):     64000000000
-MByte/s:                34625.88
-Cycles per update:      2.079345
-Cycles per cacheline:   16.634761
+Number of Flops:        1250000000
+MFlops/s:               904.96
+Data volume (Byte):     20000000000
+MByte/s:                14479.33
+Cycles per update:      5.746094
+Cycles per cacheline:   45.968754
 Loads per update:       3
 Stores per update:      1
 Load bytes per element: 24
 Store bytes per elem.:  8
 Load/store ratio:       3.00
-Instructions:           9500000016
-UOPs:                   15000000000
+Instructions:           2968750016
+UOPs:                   4687500000
 --------------------------------------------------------------------------------
 ```
 
 ### Memory bandwidth (MByte/s) on a full node
 ```
-likwid-bench -t triad -W N:2GB:128
+likwid-bench -t triad -W N:2GB:16
 ```
 ```
 ====
-Starting job 8113873 at Thu  1 May 14:08:55 BST 2025 for user dc-niko3.
-Running on nodes: m5002
-====
-Warning: Sanitizing vector length to a multiple of the loop stride 4 and thread count 128 from 62500000 elements (500000000 bytes) to 62499840 elements (499998720 bytes)
-Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
-Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
-Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
-Allocate: Process running on hwthread 91 (Domain N) - Vector length 62499840/499998720 Offset 0 Alignment 512
-Initialization: Each thread in domain initializes its own stream chunks
---------------------------------------------------------------------------------
+Starting job ...
+...
 LIKWID MICRO BENCHMARK
 Test: triad
---------------------------------------------------------------------------------
+...
 Using 1 work groups
-Using 128 threads
---------------------------------------------------------------------------------
+Using 16 threads
 ...
---------------------------------------------------------------------------------
-Cycles:                 438323588617
-CPU Clock:              2249977234
-Cycle Clock:            2249977234
-Time:                   1.948125e+02 sec
-Iterations:             524288
-Iterations per thread:  4096
-Inner loop executions:  122070
-Size (Byte):            1999994880
-Size per thread:        15624960
-Number of Flops:        511998689280
-MFlops/s:               2628.16
-Data volume (Byte):     8191979028480
-MByte/s:                42050.59
-Cycles per update:      1.712206
-Cycles per cacheline:   13.697647
+Cycles:                 25008707817
+CPU Clock:              2599930277
+Cycle Clock:            2599930277
+Time:                   9.618992e+00 sec
+Iterations:             2048
+Iterations per thread:  128
+Inner loop executions:  976562
+Size (Byte):            1999998976
+Size per thread:        124999936
+Number of Flops:        15999991808
+MFlops/s:               1663.38
+Data volume (Byte):     255999868928
+MByte/s:                26614.00
+Cycles per update:      3.126090
+Cycles per cacheline:   25.008721
 Loads per update:       3
 Stores per update:      1
 Load bytes per element: 24
 Store bytes per elem.:  8
 Load/store ratio:       3.00
-Instructions:           1215996887056
-UOPs:                   1919995084800
+Instructions:           37999980560
+UOPs:                   59999969280
 --------------------------------------------------------------------------------
 ```
 
-### Peak floating-point performance (FLOPS) on single core
+### Peak floating-point performance (MFLOPS) on a single core
 ```
 likwid-bench -t peakflops -w S0:10000MB:1
 ```
 ```
 ====
-Starting job 8113854 at Thu  1 May 14:01:16 BST 2025 for user dc-niko3.
-Running on nodes: m5002
-====
-Allocate: Process running on hwthread 91 (Domain S0) - Vector length 1250000000/10000000000 Offset 0 Alignment 512
---------------------------------------------------------------------------------
+Starting job ...
+...
 LIKWID MICRO BENCHMARK
 Test: peakflops
---------------------------------------------------------------------------------
+...
 Using 1 work groups
 Using 1 threads
---------------------------------------------------------------------------------
---------------------------------------------------------------------------------
-Group: 0 Thread 0 Global Thread 0 running on hwthread 91 - Vector length 1250000000 Offset 0
---------------------------------------------------------------------------------
-Cycles:                 55109423925
-CPU Clock:              2249976828
-Cycle Clock:            2249976828
-Time:                   2.449333e+01 sec
+...
+Cycles:                 105343024434
+CPU Clock:              2599963429
+Cycle Clock:            2599963429
+Time:                   4.051712e+01 sec
 Iterations:             10
 Iterations per thread:  10
 Inner loop executions:  1250000000
 Size (Byte):            10000000000
 Size per thread:        10000000000
 Number of Flops:        200000000000
-MFlops/s:               8165.49
+MFlops/s:               4936.19
 Data volume (Byte):     100000000000
-MByte/s:                4082.74
-Cycles per update:      4.408754
-Cycles per cacheline:   35.270031
+MByte/s:                2468.09
+Cycles per update:      8.427442
+Cycles per cacheline:   67.419536
+Loads per update:       1
+Stores per update:      0
+Load bytes per element: 8
+Store bytes per elem.:  0
+Instructions:           250000000032
+UOPs:                   237500000000
+--------------------------------------------------------------------------------
+```
+
+### Peak floating-point performance (MFLOPS) on a full node
+```
+likwid-bench -t peakflops -w N:10000MB:16
+```
+```
+====
+Starting job ...
+...
+LIKWID MICRO BENCHMARK
+Test: peakflops
+...
+Using 1 work groups
+Using 16 threads
+...
+Cycles:                 11756247712
+CPU Clock:              2599984198
+Cycle Clock:            2599984198
+Time:                   4.521661e+00 sec
+Iterations:             160
+Iterations per thread:  10
+Inner loop executions:  78125000
+Size (Byte):            10000000000
+Size per thread:        625000000
+Number of Flops:        200000000000
+MFlops/s:               44231.53
+Data volume (Byte):     100000000000
+MByte/s:                22115.77
+Cycles per update:      0.940500
+Cycles per cacheline:   7.523999
 Loads per update:       1
 Stores per update:      0
 Load bytes per element: 8
@@ -192,14 +205,6 @@ Group 1: MEM
 |      CAS_COUNT_WR     |  MBOX6C1 |      1830926 |
 |      CAS_COUNT_RD     |  MBOX7C0 |      2019975 |
 |      CAS_COUNT_WR     |  MBOX7C1 |      1867911 |
-|      CAS_COUNT_RD     |  MBOX8C0 |       -      |
-|      CAS_COUNT_WR     |  MBOX8C1 |       -      |
-|      CAS_COUNT_RD     |  MBOX9C0 |       -      |
-|      CAS_COUNT_WR     |  MBOX9C1 |       -      |
-|      CAS_COUNT_RD     | MBOX10C0 |       -      |
-|      CAS_COUNT_WR     | MBOX10C1 |       -      |
-|      CAS_COUNT_RD     | MBOX11C0 |       -      |
-|      CAS_COUNT_WR     | MBOX11C1 |       -      |
 +-----------------------+----------+--------------+
 
 +-----------------------------------+------------+
@@ -221,12 +226,11 @@ Group 1: MEM
 ##### 1000 x 1000 humans on 100 x 100 km for minimum(100 steps, 10 minutes):
 ```
 likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
-
 ```
 ```
---------------------------------------------------------------------------------                                                 
-Group 1: MEM                                                                              
-+-----------------------+----------+----------------
+--------------------------------------------------------------------------------
+Group 1: MEM
++-----------------------+----------+----------------+
 |         Event         |  Counter |   HWThread 0   |
 +-----------------------+----------+----------------+
 |   INSTR_RETIRED_ANY   |   FIXC0  | 10078700900771 |
@@ -249,14 +253,6 @@ Group 1: MEM
 |      CAS_COUNT_WR     |  MBOX6C1 |      198253434 |
 |      CAS_COUNT_RD     |  MBOX7C0 |      296656051 |
 |      CAS_COUNT_WR     |  MBOX7C1 |      198468244 |
-|      CAS_COUNT_RD     |  MBOX8C0 |        -       |
-|      CAS_COUNT_WR     |  MBOX8C1 |        -       |
-|      CAS_COUNT_RD     |  MBOX9C0 |        -       |
-|      CAS_COUNT_WR     |  MBOX9C1 |        -       |
-|      CAS_COUNT_RD     | MBOX10C0 |        -       |
-|      CAS_COUNT_WR     | MBOX10C1 |        -       |
-|      CAS_COUNT_RD     | MBOX11C0 |        -       |
-|      CAS_COUNT_WR     | MBOX11C1 |        -       |
 +-----------------------+----------+----------------+
 
 +-----------------------------------+------------+
@@ -315,7 +311,7 @@ Group 1: FLOPS_DP
 
 ##### 1000 x 1000 humans on 100 x 100 km for minimum(100 steps, 10 minutes):
 ```
-likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml # -A -s -g -G
+likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
 ```
 ```
 --------------------------------------------------------------------------------
@@ -378,30 +374,30 @@ Group 1: FLOPS_DP
 
 | Metric         | SWIFT (best case) | likwid-bench triad | Ratio      |
 |----------------|------------------|--------------------|------------|
-| Memory BW      | 205.97 MB/s      | 34,625.88 MB/s     | ~0.6%      |
+| Memory BW      | 205.97 MB/s      | 26614.00 MB/s      | ~0.77%     |
 | Read/Write Ratio | 1.1:1          | 3:1                | Lower      |
-| CPI            | 0.4615           | 2.079              | ~4.5x better |
+| CPI            | 0.4615           | 3.13               | ~6.8x better |
 
 ---
 
 3. **Comparison with `likwid-bench peakflops`**
 
-| Metric         | SWIFT (best case) | likwid-bench peakflops | Ratio      |
-|----------------|------------------|------------------------|------------|
-| MFLOP/s        | 177.26           | 8165.49                | ~2.17%     |
-| Vectorization  | 62.25%           | ~100%                  | ~0.62x     |
-| CPI            | 0.4629           | ~4.41                  | ~9.5x better |
+| Metric         | SWIFT (best case) | likwid-bench peakflops (single core) | likwid-bench peakflops (full node) | Ratio (single core) | Ratio (full node) |
+|----------------|------------------|--------------------------------------|------------------------------------|---------------------|-------------------|
+| MFLOP/s        | 177.26           | 4936.19                              | 44231.53                           | ~3.6%               | ~0.4%             |
+| Vectorization  | 62.25%           | ~100%                                | ~100%                              | ~0.62x              | ~0.62x            |
+| CPI            | 0.4629           | 8.43                                 | 0.94                               | ~18x better         | ~2x better        |
 
 ---
 
 **Summary Table**
 
-| Metric                | SWIFT (large, best case) | triad             | peakflops         |
-|-----------------------|-------------------------|-------------------|-------------------|
-| Memory BW [MB/s]      | 205.97                  | 34,625.88         | 4082.74           |
-| DP MFLOP/s            | 65.69                   | (not measured)    | 8165.49           |
-| CPI                   | 0.4615                  | 2.079             | ~4.41             |
-| Vectorization [%]     | 62.25                   | (not measured)    | ~100              |
+| Metric                | SWIFT (large, best case) | triad (single core) | triad (full node) | peakflops (single core) | peakflops (full node) |
+|-----------------------|-------------------------|---------------------|-------------------|-------------------------|-----------------------|
+| Memory BW [MB/s]      | 205.97                  | 14479.33            | 26614.00          | 2468.09                 | 22115.77              |
+| DP MFLOP/s            | 65.69                   | (not measured)      | (not measured)    | 4936.19                 | 44231.53              |
+| CPI                   | 0.4615                  | 3.13                | 3.13              | 8.43                    | 0.94                  |
+| Vectorization [%]     | 62.25                   | (not measured)      | (not measured)    | ~100                    | ~100                  |
 
 ---
 
@@ -409,7 +405,7 @@ Group 1: FLOPS_DP
 
 - **Scaling:** Memory bandwidth and data volume increase with problem size, but bandwidth is still far from hardware peak.
 - **Compared to triad:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
-- **Compared to peakflops:** SWIFT achieves only ~2% of peak FLOPS, and about 5% of peakflops memory bandwidth, with much lower vectorization.
+- **Compared to peakflops:** SWIFT achieves only ~3.6% of single-core peak FLOPS, and about 0.4% of full-node peakflops MFLOPS, with much lower vectorization.
 - **Optimization Potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
 
 ---
@@ -705,12 +701,12 @@ Group: 1
 
 | Metric         | swift_mpi (best rank) | likwid-bench triad | Ratio      |
 |----------------|----------------------|--------------------|------------|
-| Memory BW      | 1515 MB/s            | 34,625.88 MB/s     | ~4.4%      |
+| Memory BW      | 1515 MB/s            | 26614.00 MB/s      | ~5.7%      |
 | Read/Write Ratio | ~1.8:1             | 3:1                | Lower      |
-| CPI            | 0.84                 | 2.08               | ~2.5x better |
+| CPI            | 0.84                 | 3.13               | ~3.7x better |
 
 - **Observation:**  
-  - SWIFT achieves only ~4.4% of the streaming memory bandwidth of triad.
+  - SWIFT achieves only ~5.7% of the streaming memory bandwidth of triad.
   - Access pattern is less streaming (lower read/write ratio).
   - CPI is better in SWIFT, but this is expected for memory-bound code.
 
@@ -720,13 +716,13 @@ Group: 1
 
 | Metric         | swift_mpi (best rank) | likwid-bench peakflops | Ratio      |
 |----------------|----------------------|------------------------|------------|
-| MFLOP/s        | ~62                  | 8165.49                | ~0.76%     |
-| Memory BW      | 1515 MB/s            | 4082.74 MB/s           | ~37%       |
-| CPI            | 0.84                 | ~4.41                  | ~5x better |
-| Vectorization  | ~22–34%              | ~100%                  | ~0.22–0.34x|
+| MFLOP/s        | ~82.7                | 4936.19                | ~1.7%      |
+| Memory BW      | 1515 MB/s            | 2468.09 MB/s           | ~61%       |
+| CPI            | 0.84                 | 8.43                   | ~10x better |
+| Vectorization  | ~67%                 | ~100%                  | ~0.67x     |
 
 - **Observation:**  
-  - SWIFT achieves less than 1% of peak FLOPS and about 37% of peakflops memory bandwidth.
+  - SWIFT achieves about 1.7% of peak FLOPS and about 61% of peakflops memory bandwidth.
   - Vectorization ratio is much lower than peakflops.
   - CPI is much better in SWIFT, but peakflops is compute-bound.
 
@@ -736,10 +732,10 @@ Group: 1
 
 | Metric                | swift_mpi (large, best rank) | triad             | peakflops         |
 |-----------------------|------------------------------|-------------------|-------------------|
-| Memory BW [MB/s]      | 1515                         | 34,625.88         | 4082.74           |
-| DP MFLOP/s            | ~44                          | (not measured)    | 8165.49           |
-| CPI                   | 0.84                         | 2.08              | ~4.41             |
-| Vectorization [%]     | ~22–34                       | (not measured)    | ~100              |
+| Memory BW [MB/s]      | 1515                         | 26614.00          | 2468.09           |
+| DP MFLOP/s            | ~82.7                        | (not measured)    | 4936.19           |
+| CPI                   | 0.84                         | 3.13              | 8.43              |
+| Vectorization [%]     | ~67                          | (not measured)    | ~100              |
 
 ---
 
@@ -747,7 +743,7 @@ Group: 1
 
 - **Scaling:** Memory bandwidth and data volume increase with problem size, but bandwidth is still far from hardware peak.
 - **Compared to triad:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
-- **Compared to peakflops:** SWIFT achieves less than 1% of peak FLOPS, and about 37% of peakflops memory bandwidth, with much lower vectorization.
+- **Compared to peakflops:** SWIFT achieves about 1.7% of peak FLOPS, and about 61% of peakflops memory bandwidth, with much lower vectorization.
 - **Optimization Potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
 
 ---

From 45dd78d48bdd1f149d09365c295b1b7e14dfc48d Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Wed, 14 May 2025 18:36:04 +0100
Subject: [PATCH 25/43] Add generating multiple rivers (by replicating them on
 extended map) using multithreading

---
 examples/HumanMobility/humanMobility.yml      |   8 +-
 .../HumanMobility/humanMobility_template.yml  |   4 +-
 examples/HumanMobility/job.sh                 |   8 +-
 .../{makeRandomRiver.py => makeRivers.py}     | 131 +++++++++++++---
 examples/HumanMobility/perf/paws.md           |  20 +--
 .../HumanMobility/plot_velocity_parallel.py   | 147 ++++++------------
 examples/HumanMobility/run.sh                 |  62 ++++----
 examples/HumanMobility/visualise.sh           |  17 +-
 src/abm/Geography/river/potential.h           | 144 ++++++++---------
 9 files changed, 298 insertions(+), 243 deletions(-)
 rename examples/HumanMobility/{makeRandomRiver.py => makeRivers.py} (63%)

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index c8e6a076e..41d3a1663 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -15,10 +15,10 @@ TimeIntegration:
 
 # Parameters governing the snapshots
 Snapshots:
-  subdir:              data-3
+  subdir:              data-rivers-3
   basename:            humanMobility # Common part of the name of output files
   time_first:          0.         # Time of the first output (in internal units)
-  delta_time:          1.0 #1e-2 #1440        # Time difference between consecutive outputs (in internal units)
+  delta_time:          1e-2 #1440        # Time difference between consecutive outputs (in internal units)
 
 # Parameters governing the conserved quantities statistics
 Statistics:
@@ -36,7 +36,7 @@ Scheduler:
 
 # Parameters related to the initial conditions
 InitialConditions:
-  file_name:  humans-3.hdf5     # The file to read
+  file_name:  humans-rivers-3.hdf5     # The file to read
   periodic:   1
 
 PhysicalConstants:
@@ -72,4 +72,4 @@ Gravity:
 RiverPotential:
 #  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
 #  mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  river-3.hdf5 # Path to acceleration field HDF5 file
+  parameter_file:  river-rivers-3.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/humanMobility_template.yml b/examples/HumanMobility/humanMobility_template.yml
index 776da6cda..98d1f4851 100644
--- a/examples/HumanMobility/humanMobility_template.yml
+++ b/examples/HumanMobility/humanMobility_template.yml
@@ -18,7 +18,7 @@ Snapshots:
   subdir:              ${DATA}
   basename:            ${HUMANMOBILITY} # Common part of the name of output files
   time_first:          0.         # Time of the first output (in internal units)
-  delta_time:          1.0 #1e-2 #1440        # Time difference between consecutive outputs (in internal units)
+  delta_time:          1e-2 #1440        # Time difference between consecutive outputs (in internal units)
 
 # Parameters governing the conserved quantities statistics
 Statistics:
@@ -72,4 +72,4 @@ Gravity:
 RiverPotential:
 #  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
 #  mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  ${RIVER}.hdf5 # Path to acceleration field HDF5 file
+  parameter_file:  ${RIVERS}.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index dda41b295..d0465c672 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -1,13 +1,13 @@
 #!/bin/bash
 
-#SBATCH --ntasks=4             # Total number of MPI tasks (cores) (max 32)
+#SBATCH --ntasks=1             # Total number of MPI tasks (cores) (max 16)
 #SBATCH --nodes=1               # Number of nodes
-#SBATCH --ntasks-per-node=4    # MPI tasks per node (max 32)
-#SBATCH --cpus-per-task=4      # CPU cores per MPI rank
+#SBATCH --ntasks-per-node=1    # MPI tasks per node (max 16)
+#SBATCH --cpus-per-task=16      # CPU cores per MPI rank
 #SBATCH --mem=120G               # Memory per node
 #SBATCH -p cosma               # COSMA5 partition
 #SBATCH -A durham               # Account
-#SBATCH -t 1-00:00:00
+#SBATCH -t 2-00:00:00
 #SBATCH --mail-type=END
 #SBATCH --mail-user=lcgk69@durham.ac.uk
 
diff --git a/examples/HumanMobility/makeRandomRiver.py b/examples/HumanMobility/makeRivers.py
similarity index 63%
rename from examples/HumanMobility/makeRandomRiver.py
rename to examples/HumanMobility/makeRivers.py
index 224d31460..b63253dae 100644
--- a/examples/HumanMobility/makeRandomRiver.py
+++ b/examples/HumanMobility/makeRivers.py
@@ -3,9 +3,13 @@
 import argparse as ap
 from scipy.ndimage import gaussian_filter
 from scipy.interpolate import splprep, splev
+import concurrent.futures
+import time
 
 def generate_meandering_river(box_size, num_control_points=8, river_width=60.0, randomness=0.15):
     """Generate a meandering river using control points and spline interpolation"""
+
+    print("Generating meandering river...")
     
     # Generate random control points for river centerline
     # Start from left side (west)
@@ -54,7 +58,7 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
     - Within 'distance' from banks: acceleration proportional to distance constant
     - Outside: acceleration proportional to actual distance from closest bank
     """
-    
+
     # Find distances and closest points on both banks
     left_dists = np.sqrt((x - left_bank_x)**2 + (y - left_bank_y)**2)
     right_dists = np.sqrt((x - right_bank_x)**2 + (y - right_bank_y)**2)
@@ -138,6 +142,26 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
     
     return ax, ay
 
+def compute_row(i, x, y_coords, river_segments, mass, distance):
+    ax_row = np.zeros(len(y_coords), dtype=np.float32)
+    ay_row = np.zeros(len(y_coords), dtype=np.float32)
+    for j, y in enumerate(y_coords):
+        min_dist = float('inf')
+        best_seg = None
+        for seg in river_segments:
+            seg_y = np.mean(seg["centerline_y"])
+            dist = abs(y - seg_y)
+            if dist < min_dist:
+                min_dist = dist
+                best_seg = seg
+        ax_row[j], ay_row[j] = calculate_river_acceleration(
+            x, y,
+            best_seg["left_bank_x"], best_seg["left_bank_y"],
+            best_seg["right_bank_x"], best_seg["right_bank_y"],
+            mass, distance
+        )
+    return i, ax_row, ay_row
+
 def main():
     # Parse arguments
     parser = ap.ArgumentParser()
@@ -155,18 +179,71 @@ def main():
     np.random.seed(args.seed)
     
     # Parameters
-    box_size = [args.box_size, args.box_size]  # square domain
+    max_box = min(args.box_size, 10000.0)
+    box_size = [max_box, max_box]  # restrict to [0, min(args.box_size, 10000)]
     mass = 1e4                    # "mass" of the river
     distance = 1.0                # minimal distance from river
     river_width = 200.0           # width of the river
     
     # Grid parameters
     grid_size = [args.grid_size+1, args.grid_size+1]  # square grid
-    
-    # Generate meandering river
-    x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y = \
-        generate_meandering_river(box_size, river_width=river_width)
-    
+
+    river_segments = []
+    block_size = 10000.0
+    start_time = time.time()
+    if args.box_size > block_size:
+        n_repeat_x = int(np.ceil(args.box_size / block_size))
+        n_repeat_y = int(np.ceil(args.box_size / block_size))
+        blocks = []
+        for j in range(n_repeat_y):
+            for i in range(n_repeat_x):
+                offset_x = i * block_size
+                offset_y = j * block_size
+                # Compute actual block size (may be smaller at edges)
+                bx = min(block_size, args.box_size - offset_x)
+                by = min(block_size, args.box_size - offset_y)
+                if bx > 0 and by > 0:
+                    blocks.append((offset_x, offset_y, [bx, by]))
+
+        def generate_block_river(args_tuple):
+            offset_x, offset_y, block_box_size = args_tuple
+            # Each block gets its own river
+            x_c, y_c, l_x, l_y, r_x, r_y = generate_meandering_river(block_box_size, river_width=river_width)
+            # Only keep points within the block
+            mask = (x_c >= 0) & (x_c <= block_box_size[0])
+            x_c = x_c[mask] + offset_x
+            y_c = y_c[mask] + offset_y
+            l_x = l_x[mask] + offset_x
+            l_y = l_y[mask] + offset_y
+            r_x = r_x[mask] + offset_x
+            r_y = r_y[mask] + offset_y
+            return {
+                "centerline_x": x_c,
+                "centerline_y": y_c,
+                "left_bank_x": l_x,
+                "left_bank_y": l_y,
+                "right_bank_x": r_x,
+                "right_bank_y": r_y,
+            }
+
+        print("Generating rivers in parallel...")
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            river_segments = list(executor.map(generate_block_river, blocks))
+        box_size = [args.box_size, args.box_size]
+    else:
+        x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y = \
+            generate_meandering_river(box_size, river_width=river_width)
+        river_segments.append({
+            "centerline_x": x_center,
+            "centerline_y": y_center,
+            "left_bank_x": left_bank_x,
+            "left_bank_y": left_bank_y,
+            "right_bank_x": right_bank_x,
+            "right_bank_y": right_bank_y,
+        })
+    end_time = time.time()
+    print(f"River generation took {end_time - start_time:.2f} seconds.")
+
     # Create acceleration field arrays
     ax = np.zeros(grid_size, dtype=np.float32)
     ay = np.zeros(grid_size, dtype=np.float32)
@@ -177,15 +254,21 @@ def main():
     
     # Calculate acceleration field at each grid point
     print("Calculating acceleration field...")
-    for i, x in enumerate(x_coords):
-        if i % 100 == 0:  # Progress indicator
+    accel_start_time = time.time()
+    with concurrent.futures.ProcessPoolExecutor() as executor:
+        futures = []
+        for i, x in enumerate(x_coords):
+            # if i % 20 == 0:  # Progress indicator
             print(f"Processing row {i}/{grid_size[0]}")
-        for j, y in enumerate(y_coords):
-            ax[i,j], ay[i,j] = calculate_river_acceleration(
-                x, y, left_bank_x, left_bank_y, right_bank_x, right_bank_y,
-                mass, distance
-            )
-    
+            # Submit tasks to the executor
+            futures.append(executor.submit(compute_row, i, x, y_coords, river_segments, mass, distance))
+        for future in concurrent.futures.as_completed(futures):
+            i, ax_row, ay_row = future.result()
+            ax[i, :] = ax_row
+            ay[i, :] = ay_row
+    accel_end_time = time.time()
+    print(f"Acceleration field calculation took {accel_end_time - accel_start_time:.2f} seconds.")
+
     # Optional: Smooth the acceleration field
     # print("Smoothing acceleration field...")
     # ax = gaussian_filter(ax, sigma=1.0)
@@ -198,13 +281,17 @@ def main():
         f.create_dataset("AccelerationField/ax", data=ax)
         f.create_dataset("AccelerationField/ay", data=ay)
         
-        # Save river geometry for visualization
-        f.create_dataset("RiverGeometry/centerline_x", data=x_center)
-        f.create_dataset("RiverGeometry/centerline_y", data=y_center)
-        f.create_dataset("RiverGeometry/left_bank_x", data=left_bank_x)
-        f.create_dataset("RiverGeometry/left_bank_y", data=left_bank_y)
-        f.create_dataset("RiverGeometry/right_bank_x", data=right_bank_x)
-        f.create_dataset("RiverGeometry/right_bank_y", data=right_bank_y)
+        # Save river geometry for visualization as a collection
+        river_group = f.create_group("RiverGeometry")
+        for idx, seg in enumerate(river_segments):
+            grp = river_group.create_group(f"river_{idx}")
+            grp.create_dataset("centerline_x", data=seg["centerline_x"])
+            grp.create_dataset("centerline_y", data=seg["centerline_y"])
+            grp.create_dataset("left_bank_x", data=seg["left_bank_x"])
+            grp.create_dataset("left_bank_y", data=seg["left_bank_y"])
+            grp.create_dataset("right_bank_x", data=seg["right_bank_x"])
+            grp.create_dataset("right_bank_y", data=seg["right_bank_y"])
+        river_group.attrs["num_rivers"] = len(river_segments)
         
         # Save metadata
         f.create_group("Header")
diff --git a/examples/HumanMobility/perf/paws.md b/examples/HumanMobility/perf/paws.md
index 82da5d5c9..b57405faa 100644
--- a/examples/HumanMobility/perf/paws.md
+++ b/examples/HumanMobility/perf/paws.md
@@ -404,9 +404,9 @@ Group 1: FLOPS_DP
 **Key Takeaways**
 
 - **Scaling:** Memory bandwidth and data volume increase with problem size, but bandwidth is still far from hardware peak.
-- **Compared to triad:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
-- **Compared to peakflops:** SWIFT achieves only ~3.6% of single-core peak FLOPS, and about 0.4% of full-node peakflops MFLOPS, with much lower vectorization.
-- **Optimization Potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
+- **Compared to `triad`:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
+- **Compared to `peakflops`:** SWIFT achieves only ~3.6% of single-core peak FLOPS, and about 0.4% of full-node `peakflops` MFLOPS, with much lower vectorization.
+- **Optimization potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
 
 ---
 
@@ -706,7 +706,7 @@ Group: 1
 | CPI            | 0.84                 | 3.13               | ~3.7x better |
 
 - **Observation:**  
-  - SWIFT achieves only ~5.7% of the streaming memory bandwidth of triad.
+  - SWIFT achieves only ~5.7% of the streaming memory bandwidth of `triad`.
   - Access pattern is less streaming (lower read/write ratio).
   - CPI is better in SWIFT, but this is expected for memory-bound code.
 
@@ -722,9 +722,9 @@ Group: 1
 | Vectorization  | ~67%                 | ~100%                  | ~0.67x     |
 
 - **Observation:**  
-  - SWIFT achieves about 1.7% of peak FLOPS and about 61% of peakflops memory bandwidth.
-  - Vectorization ratio is much lower than peakflops.
-  - CPI is much better in SWIFT, but peakflops is compute-bound.
+  - SWIFT achieves about 1.7% of peak FLOPS and about 61% of `peakflops` memory bandwidth.
+  - Vectorization ratio is much lower than `peakflops`.
+  - CPI is much better in SWIFT, but `peakflops` is compute-bound.
 
 ---
 
@@ -742,9 +742,9 @@ Group: 1
 **Key Takeaways**
 
 - **Scaling:** Memory bandwidth and data volume increase with problem size, but bandwidth is still far from hardware peak.
-- **Compared to triad:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
-- **Compared to peakflops:** SWIFT achieves about 1.7% of peak FLOPS, and about 61% of peakflops memory bandwidth, with much lower vectorization.
-- **Optimization Potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
+- **Compared to `triad`:** SWIFT achieves only a small fraction of the streaming bandwidth, with a more balanced (less streaming) access pattern and better CPI.
+- **Compared to `peakflops`:** SWIFT achieves about 1.7% of peak FLOPS, and about 61% of `peakflops` memory bandwidth, with much lower vectorization.
+- **Optimization potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
 
 ---
 
diff --git a/examples/HumanMobility/plot_velocity_parallel.py b/examples/HumanMobility/plot_velocity_parallel.py
index 23c08ed0d..4ca439a11 100644
--- a/examples/HumanMobility/plot_velocity_parallel.py
+++ b/examples/HumanMobility/plot_velocity_parallel.py
@@ -12,160 +12,115 @@
 import multiprocessing
 from functools import partial
 
-def process_file(i, min_x, max_x, min_y, max_y, _type="gas"):
+def process_file(i, min_x, max_x, min_y, max_y, _type="gas", river_base="river", data_base="data/humanMobility", image_base="images/humanMobility"):
+    river_filename = f"{river_base}.hdf5"
+    filename_hdf5 = f"{data_base}_{i:04d}.hdf5"
+    filename_png = f"{image_base}_{i:04d}.png"
 
-    with h5py.File("river.hdf5", 'r') as sim:
+    # Ensure the output directory exists
+    os.makedirs(os.path.dirname(filename_png), exist_ok=True)
+
+    with h5py.File(river_filename, 'r') as sim:
         mass = sim["/Header"].attrs["Mass"]
         box_size = sim["/Header"].attrs["BoxSize"]
-        
-        # Load river geometry from HDF5 file
-        left_bank_x = sim["/RiverGeometry/left_bank_x"][:]
-        left_bank_y = sim["/RiverGeometry/left_bank_y"][:]
-        right_bank_x = sim["/RiverGeometry/right_bank_x"][:]
-        right_bank_y = sim["/RiverGeometry/right_bank_y"][:]
-        center_x = sim["/RiverGeometry/centerline_x"][:]
-        center_y = sim["/RiverGeometry/centerline_y"][:]
-
-    # Define the filename pattern for the HDF5 files and the PNG files
-    filename_hdf5 = "data/humanMobility_%04d.hdf5" % i  # Adjust the filename pattern as needed
-    filename_png = "images/humanMobility_%04d.png" % i
-
-    # Check if the PNG file already exists to avoid reprocessing
+        river_group = sim["/RiverGeometry"]
+        num_rivers = river_group.attrs.get("num_rivers", 1)
+        river_geometries = []
+        for idx in range(num_rivers):
+            grp = river_group[f"river_{idx}"]
+            river_geometries.append({
+                "left_bank_x": grp["left_bank_x"][:],
+                "left_bank_y": grp["left_bank_y"][:],
+                "right_bank_x": grp["right_bank_x"][:],
+                "right_bank_y": grp["right_bank_y"][:],
+                "center_x": grp["centerline_x"][:],
+                "center_y": grp["centerline_y"][:],
+            })
+
     if os.path.exists(filename_png):
         print(f"File {filename_png} already exists. Skipping.")
         return
 
     # Read data from the HDF5 file
     with h5py.File(filename_hdf5, 'r') as sim:
-
-        # Read the simulation data
-        # sim = h5py.File("humanMobility_%04d.hdf5" % snap, "r")
         boxSize = sim["/Header"].attrs["BoxSize"][0]
         time = sim["/Header"].attrs["Time"][0]
-
-        # scheme = sim["/HydroScheme"].attrs["Scheme"]
-        # kernel = sim["/HydroScheme"].attrs["Kernel function"]
-        # neighbours = sim["/HydroScheme"].attrs["Kernel target N_ngb"]
-        # eta = sim["/HydroScheme"].attrs["Kernel eta"]
-
-        # point_mass = sim["/Parameters"].attrs["PointMassPotential:mass"]
-
         git = sim["Code"].attrs["Git Revision"]
-
-        # Use PartType0 for gas, PartType1 for particles
         part_type = "PartType0" if _type == "gas" else "PartType1"
-        
         pos = sim[f"/{part_type}/Coordinates"][:, :]
         x = pos[:, 0] - boxSize / 2
         y = pos[:, 1] - boxSize / 2
         vel = sim[f"/{part_type}/Velocities"][:, :]
         v_norm = np.sqrt(vel[:, 0] ** 2 + vel[:, 1] ** 2)
-        # mass = sim["/UnusedParameters"].attrs["RiverPotential:mass"]
         if _type == "gas":
-          rho = sim["/PartType0/Densities"][:]
-          u = sim["/PartType0/InternalEnergies"][:]
-          S = sim["/PartType0/Entropies"][:]
-          P = sim["/PartType0/Pressures"][:]
-
+            rho = sim["/PartType0/Densities"][:]
+            u = sim["/PartType0/InternalEnergies"][:]
+            S = sim["/PartType0/Entropies"][:]
+            P = sim["/PartType0/Pressures"][:]
 
         X = pos[:, 0]
         Y = pos[:, 1]
         U = vel[:, 0]
         V = vel[:, 1]
-
-        # Plot the interesting quantities
-        plt.figure(figsize=(20, 20 / 1.6))
-
-        # Calculate the magnitude of the velocity vectors
-        # vel_mag = np.sqrt(vel[:, 0]**2 + vel[:, 1]**2)
+        plt.figure(figsize=(200, 200 / 1.6))
         M = np.hypot(V, U)
         print("Processing %04d, min/max X=(%4.2f, %4.2f), min/max Y (%4.2f, %4.2f), min/max U=(%4.2f, %4.2f), min/max V=(%4.2f, %4.2f)" % \
               (i, np.min(X), np.max(X), np.min(Y), np.max(Y), np.min(U), np.max(U), np.min(V), np.max(V)))
         print("boxsize:", boxSize)
-
-        # Determine a suitable scale for the arrow lengths
         max_M = np.max(M)
-        desired_max_arrow_length = 0.02  # Adjust this value as needed
+        desired_max_arrow_length = 0.02
         scale = max_M / desired_max_arrow_length
-
-        # Plotting the velocity vectors using quiver
-
-        # Create the plot
         fig, ax = plt.subplots()
         ax.set_title("Velocity map of human mobility %04d (scale=%07.2d)\n \"river mass\"=%s" % (i, scale, mass))
 
         # Plot river banks and centerline
-        ax.plot(left_bank_x, left_bank_y, '-', color='0.8', linewidth=0.5, label='Left bank')
-        ax.plot(right_bank_x, right_bank_y, '-', color='0.8', linewidth=0.5, label='Right bank')
-        ax.plot(center_x, center_y, '--', color='0.6', linewidth=0.3, label='River center')
+        for river in river_geometries:
+            ax.plot(river["left_bank_x"], river["left_bank_y"], '-', color='0.8', linewidth=0.5, label=None)
+            ax.plot(river["right_bank_x"], river["right_bank_y"], '-', color='0.8', linewidth=0.5, label=None)
+            ax.plot(river["center_x"], river["center_y"], '--', color='0.6', linewidth=0.3, label=None)
 
         # Create the quiver plot
         Q = ax.quiver(
-            X,              # X positions
-            Y,              # Y positions
-            U,              # X components of velocity
-            V,              # Y components of velocity
-            M,              # Color by velocity magnitude
-            scale=scale,    # Scale for arrow lengths
-            pivot='tip',
-            # angles='xy',
-            # scale_units='xy',
-            # cmap="PuBu",
-            # width=0.025,
-            # headwidth=30,
-            # headlength=50,
-            # headaxislength=45,
-            # minshaft=20,
-            # minlength=1.0
+            X, Y, U, V, M, scale=scale, pivot='tip',
         )
-
         ax.scatter(X, Y, color='0.5', s=0.2)
-
-        # Add a colorbar to show the magnitude of velocities
-        # plt.colorbar(label='Velocity Magnitude')
-
-        # Add a colorbar associated with the quiver plot
         cbar = fig.colorbar(Q, ax=ax, label='Velocity Magnitude')
-
         qk = ax.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E', coordinates='figure')
-        # Add legend if desired
         ax.legend(loc='upper right', fontsize='x-small')
-
-        # Add labels and formatting
-        # plt.text(
-        #     0.97, 0.97, "${\\rm{Velocity~vectors}}$",
-        #     ha="right", va="top", transform=plt.gca().transAxes, backgroundcolor="w"
-        # )
         plt.xlabel("${\\rm{Position}}~x$", labelpad=0)
         plt.ylabel("${\\rm{Position}}~y$", labelpad=0)
         plt.xlim(min_x, max_x)
         plt.ylim(min_y, max_y)
-        plt.tight_layout()
-
-        plt.savefig(filename_png, dpi=200)
+        # plt.tight_layout()
+        plt.savefig(filename_png, dpi=600)
         plt.close()
 
-
 if __name__ == '__main__':
-    
-    # Constants for the arguments
-    num_files = int(sys.argv[1])   # Adjust to the number of your HDF5 files
+    # Usage: python plot_velocity_parallel.py num_files min_x max_x min_y max_y type [river_base] [data_base] [image_base]
+    num_files = int(sys.argv[1])
     min_x = int(sys.argv[2])
     max_x = int(sys.argv[3])
     min_y = int(sys.argv[4])
     max_y = int(sys.argv[5])
     _type = sys.argv[6]
+    river_base = sys.argv[7] if len(sys.argv) > 7 else "river"
+    data_base = sys.argv[8] if len(sys.argv) > 8 else "data/humanMobility"
+    image_base = sys.argv[9] if len(sys.argv) > 9 else "images/humanMobility"
 
     if _type not in ["gas", "particles"]:
         raise ValueError("type must be either 'gas' or 'particles'")
 
-    # Create a partial function with fixed additional arguments
-    partial_process_file = partial(process_file,
-                                   min_x=min_x,
-                                   max_x=max_x,
-                                   min_y=min_y,
-                                   max_y=max_y,
-                                   _type=_type)
+    partial_process_file = partial(
+        process_file,
+        min_x=min_x,
+        max_x=max_x,
+        min_y=min_y,
+        max_y=max_y,
+        _type=_type,
+        river_base=river_base,
+        data_base=data_base,
+        image_base=image_base,
+    )
 
     file_indices = list(range(num_files))
 
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 4a177ab26..93795d7d0 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -1,16 +1,25 @@
 #!/bin/bash
 
 # Basenames for this run (without extensions)
-HUMANS=humans-3
-RIVER=river-3
+HUMANS=humans-rivers-3
+RIVERS=river-rivers-3
 HUMANMOBILITY=humanMobility
-DATA=data-3
+DATA=data-rivers-3
+IMAGES=images-rivers-3
 
 # Remove previously generated data and images to regenerate the new ones
-# rm ${HUMANS}.hdf5
-# rm ${RIVER}.hdf5
-# rm data/*
-# rm images/*
+rm ${HUMANS}.hdf5
+rm ${RIVERS}.hdf5
+rm ${DATA}/*
+rm ${IMAGES}/*
+
+# Generate acceleration field for river
+if [ ! -e ${RIVERS}.hdf5 ]
+then
+    echo "Generating acceleration field for the river..."
+    # python3 makeRandomRiver.py -b 1000000 -g 100000 -f ${RIVERS}.hdf5
+    python3 makeRivers.py -b 100000 -g 10000 -f ${RIVERS}.hdf5
+fi
 
 # Generate the initial conditions if they are not present.
 if [ ! -e ${HUMANS}.hdf5 ]
@@ -18,40 +27,41 @@ then
     echo "Generating initial conditions for the human mobility box example..."
     # python3 makeIC.py -t gas -f ${HUMANS}.hdf5  # -t particles
 #     python3 makeIC.py -t gas -n 10000 -b 1000000 -f ${HUMANS}.hdf5  # -t particles
-    python3 makeIC.py -t gas -n 100 -b 10000 -f ${HUMANS}.hdf5  # -t particles
-fi
-
-# Generate acceleration field for river
-if [ ! -e ${RIVER}.hdf5 ]
-then
-    echo "Generating acceleration field for the river..."
-    # python3 makeRandomRiver.py -b 1000000 -g 100000 -f ${RIVER}.hdf5
-    python3 makeRandomRiver.py -b 10000 -g 1000 -f ${RIVER}.hdf5
+    python3 makeIC.py -t gas -n 1000 -b 100000 -f ${HUMANS}.hdf5  # -t particles
 fi
 
 # Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVER
+export DATA HUMANMOBILITY HUMANS RIVERS
 envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
 
 # Run SWIFT with SLURM environment variables
 # mpirun -np ${SLURM_NTASKS:-8} swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
 # likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g MEM -- \
-likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g FLOPS_DP -- \
-    swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
-    -A -s -g -G \
-    --hm-river \
-    --hm-randomwalk \
-    -n 1000 \
-    ${HUMANMOBILITY}.yml
+
+# likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g FLOPS_DP -- \
+#     swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
+#     -A -s -g -G \
+#     --hm-river \
+#     --hm-randomwalk \
+#     -n 10000 \
+#     ${HUMANMOBILITY}.yml
+
+#OMP_NUM_THREADS=4 mpirun -n 4 bin/bt-mz.B.x
+    # --number-processes=${SLURM_NTASKS:-8} \
+    # --envv_OMP_NUM_THREADS=4 -- bin/bt-mz.B.x
+#maqao oneview -R1 --mpi-command="mpirun -np ${SLURM_NTASKS:-8}" \
 
 # module list
 
 # gdb --args swift -g --threads=4 -n 10000 ${HUMANMOBILITY}.yml # -A -s
-# swift --hm-river --hm-randomwalk --threads=8 -n 1000 ${HUMANMOBILITY}.yml # -A -s -g -G 
+swift_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+    -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
+# mpirun -np ${SLURM_NTASKS:-8} \
+#     swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+#     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
 # likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 ${HUMANMOBILITY}.yml # -A -s -g -G
 # likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 10 ${HUMANMOBILITY}.yml # -A -s -g -G
 # likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml # -A -s -g -G
-# mpirun -np 8 swift_mpi -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml
 # swift_intel2025 -h | grep version
 # likwid-mpirun -np 8 -t 16 -omp intel -g MEM -- swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
 # likwid-perfctr -a
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
index fa2fffa51..4290d538a 100755
--- a/examples/HumanMobility/visualise.sh
+++ b/examples/HumanMobility/visualise.sh
@@ -1,16 +1,19 @@
 #!/bin/bash
 
-rm images/* # don't remove the images directory if locally
+rm images-rivers-3/* # don't remove the images directory if locally
 
-num_files=1001
-min_x=0
-max_x=20000
-min_y=0
-max_y=20000
+num_files=18
+min_x=4000
+max_x=6000
+min_y=4000
+max_y=6000
 type="gas"  # or "particles"
+river_base="river-rivers-3"
+data_base="data-rivers-3/humanMobility"
+image_base="images-rivers-3/humanMobility"
 
 # Plot the result
-python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type} # comment this line if locally
+python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type} ${river_base} ${data_base} ${image_base}
 # python3 generate_GIF.py ${num_files}
 
 # This command sets:
diff --git a/src/abm/Geography/river/potential.h b/src/abm/Geography/river/potential.h
index 7241c3cc8..096bd422b 100644
--- a/src/abm/Geography/river/potential.h
+++ b/src/abm/Geography/river/potential.h
@@ -41,9 +41,6 @@
  */
 struct external_potential {
 
-  /*! Position of the river (horizontal on the map) */
-  // double y[2]; // the northern and southern bank of the river
-
   /*! Mass */
   double mass;
 
@@ -52,15 +49,15 @@ struct external_potential {
   float *ay;
   double box_size[2];
   int grid_size[2];
-  
-  /*! River geometry data */
-  float *left_bank_x;   // Left bank x-coordinates
-  float *left_bank_y;   // Left bank y-coordinates
-  float *right_bank_x;  // Right bank x-coordinates
-  float *right_bank_y;  // Right bank y-coordinates
-  int bank_points;      // Number of points defining each bank
-};
 
+  /*! River geometry data for multiple rivers */
+  int num_rivers;             // Number of rivers
+  float **left_bank_x;        // [num_rivers][bank_points[i]]
+  float **left_bank_y;
+  float **right_bank_x;
+  float **right_bank_y;
+  int *bank_points;           // Number of points for each river
+};
 
 /**
  * @brief Computes the time-step due to the acceleration from river
@@ -139,14 +136,8 @@ __attribute__((always_inline)) INLINE static void external_gravity_acceleration(
                            rx,    ry,
                            &ax,   &ay);
 
-  // if (ax > 1e+10 || ay > 1e+10)
-  //   error("Acceleration is too high (human is inside river): %f %f", ax, ay);
-
   g->a_grav[0] += ax;
   g->a_grav[1] += ay;
-  // g->a_grav[2] = az;
-
-  // gravity_add_comoving_potential(g, value); // value ?
 }
 
 /**
@@ -174,82 +165,89 @@ static INLINE void geography_read_acceleration_field(
     struct swift_params* parameter_file, struct external_potential* potential) {
 #if defined(HAVE_HDF5)
 
-  /*! Acceleration field filename */
   char filename[DESCRIPTION_BUFFER_SIZE];
-  
-  /* Read acceleration field file path */
   parser_get_param_string(parameter_file, "RiverPotential:parameter_file", filename);
 
-  /* Load acceleration field data */
-
-  /* Open file */
   hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);
   if (file_id < 0) error("Unable to open file %s", filename);
 
-  /* Read grid size and box size */
-
-  /* Open group */
+  // Read grid size, box size, and mass
   hid_t group_id = H5Gopen(file_id, "Header", H5P_DEFAULT);
   if (group_id < 0) error("unable to open group Header.\n");
-
-  /* Read box size, grid size and mass */
   io_read_array_attribute(group_id, "BoxSize", DOUBLE, potential->box_size, 2);
   io_read_array_attribute(group_id, "GridSize", INT, potential->grid_size, 2);
   io_read_attribute(group_id, "Mass", DOUBLE, &potential->mass);
-
-  /* Close group */
   hid_t status = H5Gclose(group_id);
   if (status < 0) error("error closing group.");
 
-  /* Allocate and read acceleration fields */
+  // Allocate and read acceleration fields
   const int size = potential->grid_size[0] * potential->grid_size[1];
   potential->ax = (float*)malloc(size * sizeof(float));
   potential->ay = (float*)malloc(size * sizeof(float));
   printf("size: %d\n", size);
 
-  /* Open group */
   group_id = H5Gopen(file_id, "AccelerationField", H5P_DEFAULT);
   if (group_id < 0) error("unable to open group AccelerationField.\n");
-
-  /* Read the datasets */
   io_read_array_dataset(group_id, "ax", FLOAT, potential->ax, size);
   io_read_array_dataset(group_id, "ay", FLOAT, potential->ay, size);
-
-  /* Close group */
   status = H5Gclose(group_id);
   if (status < 0) error("error closing group.");
 
-  /* Open group for river geometry */
+  // Open RiverGeometry group and read number of rivers
   group_id = H5Gopen(file_id, "RiverGeometry", H5P_DEFAULT);
   if (group_id < 0) error("unable to open group RiverGeometry.\n");
 
-  /* Get size of bank arrays */
-  hid_t dataset = H5Dopen(group_id, "left_bank_x", H5P_DEFAULT);
-  if (dataset < 0) error("unable to open dataset left_bank_x.\n");
-  hid_t space = H5Dget_space(dataset);
-  hsize_t dims[1];
-  H5Sget_simple_extent_dims(space, dims, NULL);
-  potential->bank_points = dims[0];
-  H5Sclose(space);
-  H5Dclose(dataset);
-
-  /* Allocate memory for river geometry */
-  potential->left_bank_x = (float*)malloc(potential->bank_points * sizeof(float));
-  potential->left_bank_y = (float*)malloc(potential->bank_points * sizeof(float));
-  potential->right_bank_x = (float*)malloc(potential->bank_points * sizeof(float));
-  potential->right_bank_y = (float*)malloc(potential->bank_points * sizeof(float));
-
-  /* Read river geometry datasets */
-  io_read_array_dataset(group_id, "left_bank_x", FLOAT, potential->left_bank_x, potential->bank_points);
-  io_read_array_dataset(group_id, "left_bank_y", FLOAT, potential->left_bank_y, potential->bank_points);
-  io_read_array_dataset(group_id, "right_bank_x", FLOAT, potential->right_bank_x, potential->bank_points);
-  io_read_array_dataset(group_id, "right_bank_y", FLOAT, potential->right_bank_y, potential->bank_points);
-
-  /* Close river geometry group */
+  // Read num_rivers attribute
+  int num_rivers = 1;
+  if (H5Aexists(group_id, "num_rivers") > 0) {
+    hid_t attr = H5Aopen(group_id, "num_rivers", H5P_DEFAULT);
+    H5Aread(attr, H5T_NATIVE_INT, &num_rivers);
+    H5Aclose(attr);
+  }
+  potential->num_rivers = num_rivers;
+
+  // Allocate arrays for each river
+  potential->left_bank_x  = (float**)malloc(num_rivers * sizeof(float*));
+  potential->left_bank_y  = (float**)malloc(num_rivers * sizeof(float*));
+  potential->right_bank_x = (float**)malloc(num_rivers * sizeof(float*));
+  potential->right_bank_y = (float**)malloc(num_rivers * sizeof(float*));
+  potential->bank_points  = (int*)malloc(num_rivers * sizeof(int));
+
+  // Read each river's geometry
+  for (int i = 0; i < num_rivers; i++) {
+    char river_name[32];
+    snprintf(river_name, sizeof(river_name), "river_%d", i);
+    hid_t river_group = H5Gopen(group_id, river_name, H5P_DEFAULT);
+    if (river_group < 0) error("unable to open group %s.\n", river_name);
+
+    // Get size of bank arrays
+    hid_t dataset = H5Dopen(river_group, "left_bank_x", H5P_DEFAULT);
+    if (dataset < 0) error("unable to open dataset left_bank_x.\n");
+    hid_t space = H5Dget_space(dataset);
+    hsize_t dims[1];
+    H5Sget_simple_extent_dims(space, dims, NULL);
+    int npoints = dims[0];
+    H5Sclose(space);
+    H5Dclose(dataset);
+
+    potential->bank_points[i] = npoints;
+    potential->left_bank_x[i]  = (float*)malloc(npoints * sizeof(float));
+    potential->left_bank_y[i]  = (float*)malloc(npoints * sizeof(float));
+    potential->right_bank_x[i] = (float*)malloc(npoints * sizeof(float));
+    potential->right_bank_y[i] = (float*)malloc(npoints * sizeof(float));
+
+    io_read_array_dataset(river_group, "left_bank_x", FLOAT,  potential->left_bank_x[i],  npoints);
+    io_read_array_dataset(river_group, "left_bank_y", FLOAT,  potential->left_bank_y[i],  npoints);
+    io_read_array_dataset(river_group, "right_bank_x", FLOAT, potential->right_bank_x[i], npoints);
+    io_read_array_dataset(river_group, "right_bank_y", FLOAT, potential->right_bank_y[i], npoints);
+
+    status = H5Gclose(river_group);
+    if (status < 0) error("error closing river group.");
+  }
+
   status = H5Gclose(group_id);
   if (status < 0) error("error closing RiverGeometry group.");
 
-  /* Close file */
   status = H5Fclose(file_id);
   if (status < 0) error("error closing file.");
 
@@ -311,23 +309,25 @@ if (potential->ay != NULL) {
 
 /* Free river geometry arrays if they were allocated */
 if (potential->left_bank_x != NULL) {
+  for (int i = 0; i < potential->num_rivers; i++) {
+    if (potential->left_bank_x[i] != NULL) free(potential->left_bank_x[i]);
+    if (potential->left_bank_y[i] != NULL) free(potential->left_bank_y[i]);
+    if (potential->right_bank_x[i] != NULL) free(potential->right_bank_x[i]);
+    if (potential->right_bank_y[i] != NULL) free(potential->right_bank_y[i]);
+  }
   free(potential->left_bank_x);
-  potential->left_bank_x = NULL;
-}
-
-if (potential->left_bank_y != NULL) {
   free(potential->left_bank_y);
-  potential->left_bank_y = NULL;
-}
-
-if (potential->right_bank_x != NULL) {
   free(potential->right_bank_x);
+  free(potential->right_bank_y);
+  potential->left_bank_x = NULL;
+  potential->left_bank_y = NULL;
   potential->right_bank_x = NULL;
+  potential->right_bank_y = NULL;
 }
 
-if (potential->right_bank_y != NULL) {
-  free(potential->right_bank_y);
-  potential->right_bank_y = NULL;
+if (potential->bank_points != NULL) {
+  free(potential->bank_points);
+  potential->bank_points = NULL;
 }
 #endif
 

From 800ffff05d9a1fd48be9395572acd4dec548aec7 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Wed, 14 May 2025 19:44:07 +0100
Subject: [PATCH 26/43] Show map of rivers without humans

---
 examples/HumanMobility/job.sh         |  9 ++--
 examples/HumanMobility/map.sh         | 15 +++++++
 examples/HumanMobility/plot_rivers.py | 64 +++++++++++++++++++++++++++
 3 files changed, 85 insertions(+), 3 deletions(-)
 create mode 100755 examples/HumanMobility/map.sh
 create mode 100644 examples/HumanMobility/plot_rivers.py

diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index d0465c672..d5acb4d6e 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -13,17 +13,17 @@
 
 # Check if an argument was provided
 if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--run|--vis]"
+    echo "Usage: $0 [--run|--vis|--map]"
     exit 1
 fi
 
 mode=$1
 
 case $mode in
-    --run|--vis)
+    --run|--vis|--map)
         ;;
     *)
-        echo "Invalid argument. Use --run or --vis"
+        echo "Invalid argument. Use --run, --vis or --map"
         exit 1
         ;;
 esac
@@ -58,4 +58,7 @@ case $mode in
     --vis)
         ./visualise.sh
         ;;
+    --map)
+        ./map.sh
+        ;;
 esac
diff --git a/examples/HumanMobility/map.sh b/examples/HumanMobility/map.sh
new file mode 100755
index 000000000..c5bf31fde
--- /dev/null
+++ b/examples/HumanMobility/map.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# Remove previously generated river images (do not remove the directory itself)
+rm images-rivers-3/rivers.png 2>/dev/null
+
+# Set parameters
+min_x=0
+max_x=100000
+min_y=0
+max_y=100000
+river_base="river-rivers-3"
+output_image_base="images-rivers-3/rivers"
+
+# Plot only the rivers
+python3 plot_rivers.py ${river_base}.hdf5 ${output_image_base}.png ${min_x} ${max_x} ${min_y} ${max_y}
\ No newline at end of file
diff --git a/examples/HumanMobility/plot_rivers.py b/examples/HumanMobility/plot_rivers.py
new file mode 100644
index 000000000..5ba1b94fd
--- /dev/null
+++ b/examples/HumanMobility/plot_rivers.py
@@ -0,0 +1,64 @@
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+import sys
+import os
+
+plt.style.use("../../tools/stylesheets/mnras.mplstyle")
+
+def plot_rivers(river_filename, output_png, min_x=None, max_x=None, min_y=None, max_y=None):
+    with h5py.File(river_filename, 'r') as sim:
+        box_size = sim["/Header"].attrs["BoxSize"]
+        river_group = sim["/RiverGeometry"]
+        num_rivers = river_group.attrs.get("num_rivers", 1)
+        river_geometries = []
+        for idx in range(num_rivers):
+            grp = river_group[f"river_{idx}"]
+            river_geometries.append({
+                "left_bank_x": grp["left_bank_x"][:],
+                "left_bank_y": grp["left_bank_y"][:],
+                "right_bank_x": grp["right_bank_x"][:],
+                "right_bank_y": grp["right_bank_y"][:],
+                "center_x": grp["centerline_x"][:],
+                "center_y": grp["centerline_y"][:],
+            })
+
+    fig, ax = plt.subplots()
+    ax.set_title("River Geometry")
+
+    # Plot river banks and centerline
+    for river in river_geometries:
+        ax.plot(river["left_bank_x"], river["left_bank_y"], '-', color='b', linewidth=0.8, label=None)
+        ax.plot(river["right_bank_x"], river["right_bank_y"], '-', color='b', linewidth=0.8, label=None)
+        ax.plot(river["center_x"], river["center_y"], '--', color='k', linewidth=0.5, label=None)
+
+    if min_x is not None and max_x is not None:
+        ax.set_xlim(min_x, max_x)
+    elif "BoxSize" in locals():
+        ax.set_xlim(0, box_size[0])
+    if min_y is not None and max_y is not None:
+        ax.set_ylim(min_y, max_y)
+    elif "BoxSize" in locals():
+        ax.set_ylim(0, box_size[1])
+
+    plt.xlabel("${\\rm{Position}}~x$")
+    plt.ylabel("${\\rm{Position}}~y$")
+    plt.savefig(output_png, dpi=300)
+    plt.close()
+
+if __name__ == '__main__':
+    # Usage: python plot_rivers_only.py river_file.hdf5 output.png [min_x max_x min_y max_y]
+    if len(sys.argv) < 3:
+        print("Usage: python plot_rivers_only.py river_file.hdf5 output.png [min_x max_x min_y max_y]")
+        sys.exit(1)
+    river_filename = sys.argv[1]
+    output_png = sys.argv[2]
+    min_x = float(sys.argv[3]) if len(sys.argv) > 3 else None
+    max_x = float(sys.argv[4]) if len(sys.argv) > 4 else None
+    min_y = float(sys.argv[5]) if len(sys.argv) > 5 else None
+    max_y = float(sys.argv[6]) if len(sys.argv) > 6 else None
+
+    plot_rivers(river_filename, output_png, min_x, max_x, min_y, max_y)
\ No newline at end of file

From 79bb160ec29ddca47669e1d688fe89bef0a0e609 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Thu, 15 May 2025 00:05:21 +0100
Subject: [PATCH 27/43] Move generation of HDF5 files out of run.sh into gen.sh

---
 examples/HumanMobility/job.sh    |  9 ++++++---
 examples/HumanMobility/run.sh    | 33 +++++---------------------------
 examples/HumanMobility/submit.sh |  2 +-
 3 files changed, 12 insertions(+), 32 deletions(-)

diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index d5acb4d6e..20506f4e2 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -13,17 +13,17 @@
 
 # Check if an argument was provided
 if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--run|--vis|--map]"
+    echo "Usage: $0 [--gen|--run|--vis|--map]"
     exit 1
 fi
 
 mode=$1
 
 case $mode in
-    --run|--vis|--map)
+    --gen|--run|--vis|--map)
         ;;
     *)
-        echo "Invalid argument. Use --run, --vis or --map"
+        echo "Invalid argument. Use --gen, --run, --vis or --map"
         exit 1
         ;;
 esac
@@ -52,6 +52,9 @@ module list
 
 # Execute the appropriate script
 case $mode in
+    --gen)
+        ./gen.sh
+        ;;
     --run)
         ./run.sh
         ;;
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 93795d7d0..aafb8e61b 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -7,29 +7,6 @@ HUMANMOBILITY=humanMobility
 DATA=data-rivers-3
 IMAGES=images-rivers-3
 
-# Remove previously generated data and images to regenerate the new ones
-rm ${HUMANS}.hdf5
-rm ${RIVERS}.hdf5
-rm ${DATA}/*
-rm ${IMAGES}/*
-
-# Generate acceleration field for river
-if [ ! -e ${RIVERS}.hdf5 ]
-then
-    echo "Generating acceleration field for the river..."
-    # python3 makeRandomRiver.py -b 1000000 -g 100000 -f ${RIVERS}.hdf5
-    python3 makeRivers.py -b 100000 -g 10000 -f ${RIVERS}.hdf5
-fi
-
-# Generate the initial conditions if they are not present.
-if [ ! -e ${HUMANS}.hdf5 ]
-then
-    echo "Generating initial conditions for the human mobility box example..."
-    # python3 makeIC.py -t gas -f ${HUMANS}.hdf5  # -t particles
-#     python3 makeIC.py -t gas -n 10000 -b 1000000 -f ${HUMANS}.hdf5  # -t particles
-    python3 makeIC.py -t gas -n 1000 -b 100000 -f ${HUMANS}.hdf5  # -t particles
-fi
-
 # Render the YAML config from template
 export DATA HUMANMOBILITY HUMANS RIVERS
 envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
@@ -54,14 +31,14 @@ envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
 # module list
 
 # gdb --args swift -g --threads=4 -n 10000 ${HUMANMOBILITY}.yml # -A -s
-swift_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
-    -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
+# swift_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+#     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
 # mpirun -np ${SLURM_NTASKS:-8} \
 #     swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
 #     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
-# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 ${HUMANMOBILITY}.yml # -A -s -g -G
-# likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 10 ${HUMANMOBILITY}.yml # -A -s -g -G
-# likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml # -A -s -g -G
+# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 ${HUMANMOBILITY}.yml
+likwid-perfctr -f -C 0 -g MEM swift_intel2025 -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
+# likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml
 # swift_intel2025 -h | grep version
 # likwid-mpirun -np 8 -t 16 -omp intel -g MEM -- swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
 # likwid-perfctr -a
diff --git a/examples/HumanMobility/submit.sh b/examples/HumanMobility/submit.sh
index ae1baadd1..0a832d760 100755
--- a/examples/HumanMobility/submit.sh
+++ b/examples/HumanMobility/submit.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--run|--vis]"
+    echo "Usage: $0 [--gen|--run|--vis|--map]"
     exit 1
 fi
 

From fe92a2fd3f23e377e907d0479fab2a48c72a2728 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Mon, 19 May 2025 11:29:43 +0100
Subject: [PATCH 28/43] Now correctly replicate smaller generated map, intstall
 with IPO support and add performance results

---
 examples/HumanMobility/makeIC.py     |   7 ++
 examples/HumanMobility/makeRivers.py | 137 +++++++++++++++----------
 examples/HumanMobility/perf/paws.md  | 144 +++++++++++++++++++++++++++
 install_swift_cosma.sh               |   7 +-
 4 files changed, 238 insertions(+), 57 deletions(-)

diff --git a/examples/HumanMobility/makeIC.py b/examples/HumanMobility/makeIC.py
index df5983961..2e0a7e7ea 100644
--- a/examples/HumanMobility/makeIC.py
+++ b/examples/HumanMobility/makeIC.py
@@ -22,6 +22,7 @@
 import numpy as np
 import h5py as h5
 import write_gadget as wg
+import time
 
 
 class Humans(object):
@@ -323,7 +324,10 @@ def gen_humans_grid(meta):
         "boxsize": float(ARGS["boxsize"]),
     }
 
+    start_time = time.time()
     HUMANS = gen_humans(META)
+    gen_time = time.time()
+    print(f"Human data generation took {gen_time - start_time:.2f} seconds.")
 
     # For SPH gas particles (PartType0) or dark matter particles (PartType1)
     HUMANS.save_to_gadget(
@@ -331,5 +335,8 @@ def gen_humans_grid(meta):
         boxsize=ARGS["boxsize"], 
         type=ARGS["type"]
     )
+    end_time = time.time()
+    print(f"HDF5 writing took {end_time - gen_time:.2f} seconds.")
+    print(f"Total time: {end_time - start_time:.2f} seconds.")
     print("Initial condition generated")
 
diff --git a/examples/HumanMobility/makeRivers.py b/examples/HumanMobility/makeRivers.py
index b63253dae..eb942a0a6 100644
--- a/examples/HumanMobility/makeRivers.py
+++ b/examples/HumanMobility/makeRivers.py
@@ -142,7 +142,7 @@ def calculate_river_acceleration(x, y, left_bank_x, left_bank_y, right_bank_x, r
     
     return ax, ay
 
-def compute_row(i, x, y_coords, river_segments, mass, distance):
+def compute_row_block(i, x, y_coords, river_segments, mass, distance):
     ax_row = np.zeros(len(y_coords), dtype=np.float32)
     ay_row = np.zeros(len(y_coords), dtype=np.float32)
     for j, y in enumerate(y_coords):
@@ -150,7 +150,8 @@ def compute_row(i, x, y_coords, river_segments, mass, distance):
         best_seg = None
         for seg in river_segments:
             seg_y = np.mean(seg["centerline_y"])
-            dist = abs(y - seg_y)
+            seg_x = np.mean(seg["centerline_x"])
+            dist = np.sqrt((x - seg_x)**2 + (y - seg_y)**2)
             if dist < min_dist:
                 min_dist = dist
                 best_seg = seg
@@ -194,41 +195,26 @@ def main():
     if args.box_size > block_size:
         n_repeat_x = int(np.ceil(args.box_size / block_size))
         n_repeat_y = int(np.ceil(args.box_size / block_size))
-        blocks = []
+
+        # Generate the base river only once in the bottom left corner
+        base_box_size = [block_size, block_size]
+        x_c, y_c, l_x, l_y, r_x, r_y = generate_meandering_river(base_box_size, river_width=river_width)
+
+        # Replicate the river along X and Y axes
         for j in range(n_repeat_y):
             for i in range(n_repeat_x):
                 offset_x = i * block_size
                 offset_y = j * block_size
-                # Compute actual block size (may be smaller at edges)
-                bx = min(block_size, args.box_size - offset_x)
-                by = min(block_size, args.box_size - offset_y)
-                if bx > 0 and by > 0:
-                    blocks.append((offset_x, offset_y, [bx, by]))
-
-        def generate_block_river(args_tuple):
-            offset_x, offset_y, block_box_size = args_tuple
-            # Each block gets its own river
-            x_c, y_c, l_x, l_y, r_x, r_y = generate_meandering_river(block_box_size, river_width=river_width)
-            # Only keep points within the block
-            mask = (x_c >= 0) & (x_c <= block_box_size[0])
-            x_c = x_c[mask] + offset_x
-            y_c = y_c[mask] + offset_y
-            l_x = l_x[mask] + offset_x
-            l_y = l_y[mask] + offset_y
-            r_x = r_x[mask] + offset_x
-            r_y = r_y[mask] + offset_y
-            return {
-                "centerline_x": x_c,
-                "centerline_y": y_c,
-                "left_bank_x": l_x,
-                "left_bank_y": l_y,
-                "right_bank_x": r_x,
-                "right_bank_y": r_y,
-            }
-
-        print("Generating rivers in parallel...")
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            river_segments = list(executor.map(generate_block_river, blocks))
+                # Only keep points within the block (should always be true for the base river)
+                mask = (x_c >= 0) & (x_c <= block_size)
+                river_segments.append({
+                    "centerline_x": x_c[mask] + offset_x,
+                    "centerline_y": y_c[mask] + offset_y,
+                    "left_bank_x": l_x[mask] + offset_x,
+                    "left_bank_y": l_y[mask] + offset_y,
+                    "right_bank_x": r_x[mask] + offset_x,
+                    "right_bank_y": r_y[mask] + offset_y,
+                })
         box_size = [args.box_size, args.box_size]
     else:
         x_center, y_center, left_bank_x, left_bank_y, right_bank_x, right_bank_y = \
@@ -244,36 +230,77 @@ def generate_block_river(args_tuple):
     end_time = time.time()
     print(f"River generation took {end_time - start_time:.2f} seconds.")
 
-    # Create acceleration field arrays
-    ax = np.zeros(grid_size, dtype=np.float32)
-    ay = np.zeros(grid_size, dtype=np.float32)
-    
-    # Generate grid coordinates
-    x_coords = np.linspace(0, box_size[0], grid_size[0])
-    y_coords = np.linspace(0, box_size[1], grid_size[1])
-    
-    # Calculate acceleration field at each grid point
-    print("Calculating acceleration field...")
+    # Extended information output
+    print(f"Block size: {block_size} x {block_size}")
+    print(f"Grid size in blocks: {n_repeat_x} x {n_repeat_y}" if args.box_size > block_size else "Grid size in blocks: 1 x 1")
+    print(f"Full domain size: {box_size[0]} x {box_size[1]} (meters)")
+    print(f"Full grid size: {grid_size[0]} x {grid_size[1]}")
+
+    # Calculate number of blocks
+    n_repeat_x = int(np.ceil(args.box_size / block_size))
+    n_repeat_y = int(np.ceil(args.box_size / block_size))
+
+    # Grid parameters
+    grid_size = [args.grid_size+1, args.grid_size+1]  # e.g. 10001 x 10001
+
+    # Calculate block grid size (number of points per block, including overlap)
+    block_grid_size_x = (grid_size[0] - 1) // n_repeat_x + 1
+    block_grid_size_y = (grid_size[1] - 1) // n_repeat_y + 1
+    block_grid_size = [block_grid_size_x, block_grid_size_y]
+
+    # Generate grid coordinates for the block
+    x_block = np.linspace(0, block_size, block_grid_size[0])
+    y_block = np.linspace(0, block_size, block_grid_size[1])
+
+    # Create acceleration field arrays for a single block
+    ax_block = np.zeros(block_grid_size, dtype=np.float32)
+    ay_block = np.zeros(block_grid_size, dtype=np.float32)
+
+    # Calculate acceleration field for the bottom-left block
+    print("Calculating acceleration field for bottom-left block...")
     accel_start_time = time.time()
+
     with concurrent.futures.ProcessPoolExecutor() as executor:
         futures = []
-        for i, x in enumerate(x_coords):
-            # if i % 20 == 0:  # Progress indicator
-            print(f"Processing row {i}/{grid_size[0]}")
+        for i, x in enumerate(x_block):
+            if(i % 100 == 0):
+                print(f"Processing row {i+1}/{block_grid_size[0]}")
             # Submit tasks to the executor
-            futures.append(executor.submit(compute_row, i, x, y_coords, river_segments, mass, distance))
+            futures.append(executor.submit(compute_row_block, i, x, y_block, river_segments, mass, distance))
         for future in concurrent.futures.as_completed(futures):
             i, ax_row, ay_row = future.result()
-            ax[i, :] = ax_row
-            ay[i, :] = ay_row
+            ax_block[i, :] = ax_row
+            ay_block[i, :] = ay_row
     accel_end_time = time.time()
-    print(f"Acceleration field calculation took {accel_end_time - accel_start_time:.2f} seconds.")
+    print(f"Acceleration field calculation for block took {accel_end_time - accel_start_time:.2f} seconds.")
+
+    # Now assemble the full grid, avoiding duplicated edges
+    ax = np.zeros(grid_size, dtype=np.float32)
+    ay = np.zeros(grid_size, dtype=np.float32)
+
+    for i in range(n_repeat_x):
+        for j in range(n_repeat_y):
+            # Compute start and end indices for this block in the full grid
+            x_start = i * (block_grid_size_x - 1)
+            x_end = x_start + block_grid_size_x
+            y_start = j * (block_grid_size_y - 1)
+            y_end = y_start + block_grid_size_y
+
+            # For the last block, ensure we don't go out of bounds
+            if x_end > grid_size[0]:
+                x_end = grid_size[0]
+            if y_end > grid_size[1]:
+                y_end = grid_size[1]
+
+            # Compute the corresponding slice in the block grid
+            bx_start = 0
+            bx_end = x_end - x_start
+            by_start = 0
+            by_end = y_end - y_start
+
+            ax[x_start:x_end, y_start:y_end] = ax_block[bx_start:bx_end, by_start:by_end]
+            ay[x_start:x_end, y_start:y_end] = ay_block[bx_start:bx_end, by_start:by_end]
 
-    # Optional: Smooth the acceleration field
-    # print("Smoothing acceleration field...")
-    # ax = gaussian_filter(ax, sigma=1.0)
-    # ay = gaussian_filter(ay, sigma=1.0)
-    
     # Save to HDF5 file
     print("Saving to HDF5 file...")
     with h5.File(args.file, 'w') as f:
diff --git a/examples/HumanMobility/perf/paws.md b/examples/HumanMobility/perf/paws.md
index b57405faa..43c4b1cdf 100644
--- a/examples/HumanMobility/perf/paws.md
+++ b/examples/HumanMobility/perf/paws.md
@@ -271,6 +271,84 @@ Group 1: MEM
 +-----------------------------------+------------+
 ```
 
+##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (1000 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: MEM
++-----------------------+---------+----------------+
+|         Event         | Counter |   HWThread 0   |
++-----------------------+---------+----------------+
+|   INSTR_RETIRED_ANY   |  FIXC0  | 16237189087458 |
+| CPU_CLK_UNHALTED_CORE |  FIXC1  | 13024552770775 |
+|  CPU_CLK_UNHALTED_REF |  FIXC2  | 10265924898034 |
+|      CAS_COUNT_RD     | MBOX0C0 |     4634215368 |
+|      CAS_COUNT_WR     | MBOX0C1 |     1900293496 |
+|      CAS_COUNT_RD     | MBOX1C0 |     3896604436 |
+|      CAS_COUNT_WR     | MBOX1C1 |     1068569638 |
+|      CAS_COUNT_RD     | MBOX2C0 |     3801181672 |
+|      CAS_COUNT_WR     | MBOX2C1 |     1139534217 |
+|      CAS_COUNT_RD     | MBOX3C0 |     3903990616 |
+|      CAS_COUNT_WR     | MBOX3C1 |     1066476734 |
++-----------------------+---------+----------------+
+
++-----------------------------------+------------+
+|               Metric              | HWThread 0 |
++-----------------------------------+------------+
+|        Runtime (RDTSC) [s]        |  3994.6320 |
+|        Runtime unhalted [s]       |  5009.5327 |
+|            Clock [MHz]            |  3298.6052 |
+|                CPI                |     0.8021 |
+|  Memory read bandwidth [MBytes/s] |   260.1250 |
+|  Memory read data volume [GBytes] |  1039.1035 |
+| Memory write bandwidth [MBytes/s] |    82.9092 |
+| Memory write data volume [GBytes] |   331.1919 |
+|    Memory bandwidth [MBytes/s]    |   343.0342 |
+|    Memory data volume [GBytes]    |  1370.2954 |
++-----------------------------------+------------+
+```
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (100 steps, 10 minutes, new run `run-perf.sh`):
+```
+likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: MEM
++-----------------------+---------+----------------+
+|         Event         | Counter |   HWThread 0   |
++-----------------------+---------+----------------+
+|   INSTR_RETIRED_ANY   |  FIXC0  | 10840096634364 |
+| CPU_CLK_UNHALTED_CORE |  FIXC1  |  8686843831211 |
+|  CPU_CLK_UNHALTED_REF |  FIXC2  |  6846629378654 |
+|      CAS_COUNT_RD     | MBOX0C0 |     2484751310 |
+|      CAS_COUNT_WR     | MBOX0C1 |     1062582326 |
+|      CAS_COUNT_RD     | MBOX1C0 |     1990283397 |
+|      CAS_COUNT_WR     | MBOX1C1 |      514623483 |
+|      CAS_COUNT_RD     | MBOX2C0 |     1911186239 |
+|      CAS_COUNT_WR     | MBOX2C1 |      550733506 |
+|      CAS_COUNT_RD     | MBOX3C0 |     1991751136 |
+|      CAS_COUNT_WR     | MBOX3C1 |      513981792 |
++-----------------------+---------+----------------+
+
++-----------------------------------+------------+
+|               Metric              | HWThread 0 |
++-----------------------------------+------------+
+|        Runtime (RDTSC) [s]        |  2663.8206 |
+|        Runtime unhalted [s]       |  3341.2314 |
+|            Clock [MHz]            |  3298.6835 |
+|                CPI                |     0.8014 |
+|  Memory read bandwidth [MBytes/s] |   201.2862 |
+|  Memory read data volume [GBytes] |   536.1902 |
+| Memory write bandwidth [MBytes/s] |    63.4739 |
+| Memory write data volume [GBytes] |   169.0830 |
+|    Memory bandwidth [MBytes/s]    |   264.7600 |
+|    Memory data volume [GBytes]    |   705.2732 |
++-----------------------------------+------------+
+```
+
 #### Floating-point performance (`--enable-debug`)
 
 ##### 100 x 100 humans on 10 x 10 km for minimum(1000 steps, 10 minutes):
@@ -345,6 +423,72 @@ Group 1: FLOPS_DP
 +----------------------+------------+
 ```
 
+##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (1000 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: FLOPS_DP
++--------------------------------------+---------+----------------+
+|                 Event                | Counter |   HWThread 0   |
++--------------------------------------+---------+----------------+
+|           INSTR_RETIRED_ANY          |  FIXC0  | 16579362600938 |
+|         CPU_CLK_UNHALTED_CORE        |  FIXC1  | 13307840039017 |
+|         CPU_CLK_UNHALTED_REF         |  FIXC2  | 10488495793516 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE |   PMC0  |    52287698182 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE |   PMC1  |    69433493065 |
+|       SIMD_FP_256_PACKED_DOUBLE      |   PMC2  |    82768760959 |
++--------------------------------------+---------+----------------+
+
++-------------------------+------------+
+|          Metric         | HWThread 0 |
++-------------------------+------------+
+|   Runtime (RDTSC) [s]   |  4080.0487 |
+|   Runtime unhalted [s]  |  5118.4970 |
+|       Clock [MHz]       |  3298.8265 |
+|           CPI           |     0.8027 |
+|       DP [MFLOP/s]      |   123.7936 |
+|     AVX DP [MFLOP/s]    |    81.1449 |
+|     Packed [MUOPS/s]    |    33.1017 |
+|     Scalar [MUOPS/s]    |    17.0178 |
+| Vectorization ratio [%] |    66.0455 |
++-------------------------+------------+
+```
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (100 steps, 10 minutes, new run `run-perf.sh`):
+```
+likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: FLOPS_DP
++--------------------------------------+---------+----------------+
+|                 Event                | Counter |   HWThread 0   |
++--------------------------------------+---------+----------------+
+|           INSTR_RETIRED_ANY          |  FIXC0  | 10928203210054 |
+|         CPU_CLK_UNHALTED_CORE        |  FIXC1  |  8761053789124 |
+|         CPU_CLK_UNHALTED_REF         |  FIXC2  |  6905083815370 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE |   PMC0  |    28695343585 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE |   PMC1  |    37089227479 |
+|       SIMD_FP_256_PACKED_DOUBLE      |   PMC2  |    40266663232 |
++--------------------------------------+---------+----------------+
+
++-------------------------+------------+
+|          Metric         | HWThread 0 |
++-------------------------+------------+
+|   Runtime (RDTSC) [s]   |  2685.3508 |
+|   Runtime unhalted [s]  |  3369.6745 |
+|       Clock [MHz]       |  3298.7985 |
+|           CPI           |     0.8017 |
+|       DP [MFLOP/s]      |    95.1632 |
+|     AVX DP [MFLOP/s]    |    59.9797 |
+|     Packed [MUOPS/s]    |    25.6808 |
+|     Scalar [MUOPS/s]    |    13.8117 |
+| Vectorization ratio [%] |    65.0271 |
++-------------------------+------------+
+```
+
 ##### Comparison wrt scaling and with benchmarks
 
 ---
diff --git a/install_swift_cosma.sh b/install_swift_cosma.sh
index 8115e2c6e..42ef4761a 100755
--- a/install_swift_cosma.sh
+++ b/install_swift_cosma.sh
@@ -33,6 +33,9 @@ module load likwid/5.4.1
 
 # Set number of threads for make
 export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
+export AR=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ar
+export LD=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-link
+export RANLIB=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ranlib
 
 # The main installation script for SWIFT_ABM
 
@@ -50,7 +53,7 @@ echo "#############################"
     --program-suffix=_intel2025 \
     CFLAGS="-Wno-error=gnu-folding-constant" \
     LDFLAGS= \
-    --enable-debug \
+    --enable-ipo \
     --enable-mpi \
     --enable-parallel-hdf5 \
     --with-tbbmalloc \
@@ -61,7 +64,7 @@ echo "#############################"
     --with-ext-potential=human-mobility \
     --with-hm=all
 
-		# --enable-ipo \
+		# --enable-debug \
 	    # --with-hm=random-walk
 
 

From f00c73c49bc457c6c805d952b6658778e0e15af1 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 20 May 2025 10:05:45 +0100
Subject: [PATCH 29/43] Update perf analysis

---
 examples/HumanMobility/perf/paws.md | 270 ++++++++++++++++++++--------
 1 file changed, 193 insertions(+), 77 deletions(-)

diff --git a/examples/HumanMobility/perf/paws.md b/examples/HumanMobility/perf/paws.md
index 43c4b1cdf..e1d759067 100644
--- a/examples/HumanMobility/perf/paws.md
+++ b/examples/HumanMobility/perf/paws.md
@@ -171,8 +171,6 @@ UOPs:                   237500000000
 
 ### Serial
 
-#### Memory performance (`--enable-ipo`)
-
 #### Memory performance (`--enable-debug`)
 
 ##### 100 x 100 humans on 10 x 10 km for minimum(1000 steps, 10 minutes):
@@ -310,45 +308,6 @@ Group 1: MEM
 +-----------------------------------+------------+
 ```
 
-##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (100 steps, 10 minutes, new run `run-perf.sh`):
-```
-likwid-perfctr -f -C 0 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
-```
-```
---------------------------------------------------------------------------------
-Group 1: MEM
-+-----------------------+---------+----------------+
-|         Event         | Counter |   HWThread 0   |
-+-----------------------+---------+----------------+
-|   INSTR_RETIRED_ANY   |  FIXC0  | 10840096634364 |
-| CPU_CLK_UNHALTED_CORE |  FIXC1  |  8686843831211 |
-|  CPU_CLK_UNHALTED_REF |  FIXC2  |  6846629378654 |
-|      CAS_COUNT_RD     | MBOX0C0 |     2484751310 |
-|      CAS_COUNT_WR     | MBOX0C1 |     1062582326 |
-|      CAS_COUNT_RD     | MBOX1C0 |     1990283397 |
-|      CAS_COUNT_WR     | MBOX1C1 |      514623483 |
-|      CAS_COUNT_RD     | MBOX2C0 |     1911186239 |
-|      CAS_COUNT_WR     | MBOX2C1 |      550733506 |
-|      CAS_COUNT_RD     | MBOX3C0 |     1991751136 |
-|      CAS_COUNT_WR     | MBOX3C1 |      513981792 |
-+-----------------------+---------+----------------+
-
-+-----------------------------------+------------+
-|               Metric              | HWThread 0 |
-+-----------------------------------+------------+
-|        Runtime (RDTSC) [s]        |  2663.8206 |
-|        Runtime unhalted [s]       |  3341.2314 |
-|            Clock [MHz]            |  3298.6835 |
-|                CPI                |     0.8014 |
-|  Memory read bandwidth [MBytes/s] |   201.2862 |
-|  Memory read data volume [GBytes] |   536.1902 |
-| Memory write bandwidth [MBytes/s] |    63.4739 |
-| Memory write data volume [GBytes] |   169.0830 |
-|    Memory bandwidth [MBytes/s]    |   264.7600 |
-|    Memory data volume [GBytes]    |   705.2732 |
-+-----------------------------------+------------+
-```
-
 #### Floating-point performance (`--enable-debug`)
 
 ##### 100 x 100 humans on 10 x 10 km for minimum(1000 steps, 10 minutes):
@@ -456,40 +415,7 @@ Group 1: FLOPS_DP
 +-------------------------+------------+
 ```
 
-##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (100 steps, 10 minutes, new run `run-perf.sh`):
-```
-likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
-```
-```
---------------------------------------------------------------------------------
-Group 1: FLOPS_DP
-+--------------------------------------+---------+----------------+
-|                 Event                | Counter |   HWThread 0   |
-+--------------------------------------+---------+----------------+
-|           INSTR_RETIRED_ANY          |  FIXC0  | 10928203210054 |
-|         CPU_CLK_UNHALTED_CORE        |  FIXC1  |  8761053789124 |
-|         CPU_CLK_UNHALTED_REF         |  FIXC2  |  6905083815370 |
-| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE |   PMC0  |    28695343585 |
-| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE |   PMC1  |    37089227479 |
-|       SIMD_FP_256_PACKED_DOUBLE      |   PMC2  |    40266663232 |
-+--------------------------------------+---------+----------------+
-
-+-------------------------+------------+
-|          Metric         | HWThread 0 |
-+-------------------------+------------+
-|   Runtime (RDTSC) [s]   |  2685.3508 |
-|   Runtime unhalted [s]  |  3369.6745 |
-|       Clock [MHz]       |  3298.7985 |
-|           CPI           |     0.8017 |
-|       DP [MFLOP/s]      |    95.1632 |
-|     AVX DP [MFLOP/s]    |    59.9797 |
-|     Packed [MUOPS/s]    |    25.6808 |
-|     Scalar [MUOPS/s]    |    13.8117 |
-| Vectorization ratio [%] |    65.0271 |
-+-------------------------+------------+
-```
-
-##### Comparison wrt scaling and with benchmarks
+#### Comparison wrt scaling and with microbenchmarks
 
 ---
 
@@ -816,7 +742,7 @@ Group: 1
 +------------------------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+
 ```
 
-##### Comparison wrt scaling and with benchmarks
+#### Comparison wrt scaling and with microbenchmarks
 
 ---
 
@@ -890,6 +816,196 @@ Group: 1
 - **Compared to `peakflops`:** SWIFT achieves about 1.7% of peak FLOPS, and about 61% of `peakflops` memory bandwidth, with much lower vectorization.
 - **Optimization potential:** There is significant headroom for improving both memory access patterns and vectorization in SWIFT.
 
+#### Memory performance (`--enable-ipo`)
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (100 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0-15 -g MEM swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: MEM
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+
+|         Event         | Counter |  HWThread 0  |  HWThread 1  |  HWThread 2  |  HWThread 3  |  HWThread 4  |  HWThread 5  |  HWThread 6  |  HWThread 7  |   HWThread 8  |  HWThread 9  |  HWThread 10 |  HWThread 11 |  HWThread 12 |  HWThread 13 |  HWThread 14  |  HWThread 15 |
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+
+|   INSTR_RETIRED_ANY   |  FIXC0  | 975876943752 | 965752585140 | 993753825451 | 979073598842 | 970379226841 | 978056530864 | 963295174486 | 978504614942 | 1025397969559 | 999298716967 | 993478925405 | 998344253860 | 974925642167 | 986585240716 | 1008142361518 | 958684471716 |
+| CPU_CLK_UNHALTED_CORE |  FIXC1  | 887566984243 | 875916485053 | 900089754704 | 886926244471 | 879167838974 | 884625668090 | 871514404512 | 880467057248 |  922567316942 | 902274334852 | 897231000281 | 899206842936 | 881539711541 | 890218795683 |  905108770926 | 849800952747 |
+|  CPU_CLK_UNHALTED_REF |  FIXC2  | 768140274512 | 759043739714 | 779977106012 | 768588381990 | 761894173040 | 766590485856 | 755228198478 | 762985594280 |  799331217282 | 781861953288 | 777521797962 | 779256954554 | 763927306012 | 771449474224 |  784324764002 | 736409433578 |
+|      CAS_COUNT_RD     | MBOX0C0 |   4261444731 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    3504353741 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_WR     | MBOX0C1 |   1512708396 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |     943101388 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_RD     | MBOX1C0 |   4726480585 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    4403350275 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_WR     | MBOX1C1 |   1539877534 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    1415800887 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_RD     | MBOX2C0 |   9218646904 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    5702862507 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_WR     | MBOX2C1 |   4397646832 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    1439969824 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_RD     | MBOX3C0 |  10524023451 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    8511873950 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
+|      CAS_COUNT_WR     | MBOX3C1 |   2136606278 |            0 |            0 |            0 |            0 |            0 |            0 |            0 |    3398124012 |            0 |            0 |            0 |            0 |            0 |             0 |            0 |
++-----------------------+---------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+
+
++----------------------------+---------+----------------+--------------+---------------+--------------+
+|            Event           | Counter |       Sum      |      Min     |      Max      |      Avg     |
++----------------------------+---------+----------------+--------------+---------------+--------------+
+|   INSTR_RETIRED_ANY STAT   |  FIXC0  | 15749550082226 | 958684471716 | 1025397969559 | 9.843469e+11 |
+| CPU_CLK_UNHALTED_CORE STAT |  FIXC1  | 14214222163203 | 849800952747 |  922567316942 | 8.883889e+11 |
+|  CPU_CLK_UNHALTED_REF STAT |  FIXC2  | 12316530854784 | 736409433578 |  799331217282 | 769783178424 |
+|      CAS_COUNT_RD STAT     | MBOX0C0 |     7765798472 |            0 |    4261444731 | 4.853624e+08 |
+|      CAS_COUNT_WR STAT     | MBOX0C1 |     2455809784 |            0 |    1512708396 | 1.534881e+08 |
+|      CAS_COUNT_RD STAT     | MBOX1C0 |     9129830860 |            0 |    4726480585 | 5.706144e+08 |
+|      CAS_COUNT_WR STAT     | MBOX1C1 |     2955678421 |            0 |    1539877534 | 1.847299e+08 |
+|      CAS_COUNT_RD STAT     | MBOX2C0 |    14921509411 |            0 |    9218646904 | 9.325943e+08 |
+|      CAS_COUNT_WR STAT     | MBOX2C1 |     5837616656 |            0 |    4397646832 |    364851041 |
+|      CAS_COUNT_RD STAT     | MBOX3C0 |    19035897401 |            0 |   10524023451 | 1.189744e+09 |
+|      CAS_COUNT_WR STAT     | MBOX3C1 |     5534730290 |            0 |    3398124012 | 3.459206e+08 |
++----------------------------+---------+----------------+--------------+---------------+--------------+
+
++-----------------------------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|               Metric              | HWThread 0 | HWThread 1 | HWThread 2 | HWThread 3 | HWThread 4 | HWThread 5 | HWThread 6 | HWThread 7 | HWThread 8 | HWThread 9 | HWThread 10 | HWThread 11 | HWThread 12 | HWThread 13 | HWThread 14 | HWThread 15 |
++-----------------------------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|        Runtime (RDTSC) [s]        |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |  1025.7361 |   1025.7361 |   1025.7361 |   1025.7361 |   1025.7361 |   1025.7361 |   1025.7361 |
+|        Runtime unhalted [s]       |   341.3757 |   336.8947 |   346.1922 |   341.1293 |   338.1452 |   340.2444 |   335.2016 |   338.6449 |   354.8375 |   347.0324 |    345.0927 |    345.8526 |    339.0575 |    342.3956 |    348.1226 |    326.8501 |
+|            Clock [MHz]            |  3004.2020 |  3000.2982 |  3000.3539 |  3000.2830 |  3000.1686 |  3000.2997 |  3000.3015 |  3000.3043 |  3000.8191 |  3000.3854 |   3000.2692 |   3000.1809 |   3000.2564 |   3000.2524 |   3000.3600 |   3000.3118 |
+|                CPI                |     0.9095 |     0.9070 |     0.9057 |     0.9059 |     0.9060 |     0.9045 |     0.9047 |     0.8998 |     0.8997 |     0.9029 |      0.9031 |      0.9007 |      0.9042 |      0.9023 |      0.8978 |      0.8864 |
+|  Memory read bandwidth [MBytes/s] |  1792.6230 |          0 |          0 |          0 |          0 |          0 |          0 |          0 |  1380.3123 |          0 |           0 |           0 |           0 |           0 |           0 |           0 |
+|  Memory read data volume [GBytes] |  1838.7581 |          0 |          0 |          0 |          0 |          0 |          0 |          0 |  1415.8362 |          0 |           0 |           0 |           0 |           0 |           0 |           0 |
+| Memory write bandwidth [MBytes/s] |   598.1633 |          0 |          0 |          0 |          0 |          0 |          0 |          0 |   449.0509 |          0 |           0 |           0 |           0 |           0 |           0 |           0 |
+| Memory write data volume [GBytes] |   613.5577 |          0 |          0 |          0 |          0 |          0 |          0 |          0 |   460.6078 |          0 |           0 |           0 |           0 |           0 |           0 |           0 |
+|    Memory bandwidth [MBytes/s]    |  2390.7863 |          0 |          0 |          0 |          0 |          0 |          0 |          0 |  1829.3633 |          0 |           0 |           0 |           0 |           0 |           0 |           0 |
+|    Memory data volume [GBytes]    |  2452.3158 |          0 |          0 |          0 |          0 |          0 |          0 |          0 |  1876.4439 |          0 |           0 |           0 |           0 |           0 |           0 |           0 |
++-----------------------------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+-------------+-------------+-------------+-------------+-------------+
+
++----------------------------------------+------------+-----------+-----------+-----------+
+|                 Metric                 |     Sum    |    Min    |    Max    |    Avg    |
++----------------------------------------+------------+-----------+-----------+-----------+
+|        Runtime (RDTSC) [s] STAT        | 16411.7776 | 1025.7361 | 1025.7361 | 1025.7361 |
+|        Runtime unhalted [s] STAT       |  5467.0690 |  326.8501 |  354.8375 |  341.6918 |
+|            Clock [MHz] STAT            | 48009.0464 | 3000.1686 | 3004.2020 | 3000.5654 |
+|                CPI STAT                |    14.4402 |    0.8864 |    0.9095 |    0.9025 |
+|  Memory read bandwidth [MBytes/s] STAT |  3172.9353 |         0 | 1792.6230 |  198.3085 |
+|  Memory read data volume [GBytes] STAT |  3254.5943 |         0 | 1838.7581 |  203.4121 |
+| Memory write bandwidth [MBytes/s] STAT |  1047.2142 |         0 |  598.1633 |   65.4509 |
+| Memory write data volume [GBytes] STAT |  1074.1655 |         0 |  613.5577 |   67.1353 |
+|    Memory bandwidth [MBytes/s] STAT    |  4220.1496 |         0 | 2390.7863 |  263.7594 |
+|    Memory data volume [GBytes] STAT    |  4328.7597 |         0 | 2452.3158 |  270.5475 |
++----------------------------------------+------------+-----------+-----------+-----------+
+```
+
+#### Floating-point performance (`--enable-ipo`)
+
+##### 1000 x 1000 humans on 100 x 100 km for minimum, multiple rivers (1000 steps, 10 minutes):
+```
+likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 humanMobility.yml
+```
+```
+--------------------------------------------------------------------------------
+Group 1: FLOPS_DP
++--------------------------------------+---------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+
+|                 Event                | Counter |   HWThread 0  |   HWThread 1  |   HWThread 2  |   HWThread 3  |   HWThread 4  |   HWThread 5  |   HWThread 6  |   HWThread 7  |   HWThread 8  |   HWThread 9  |  HWThread 10  |  HWThread 11  |  HWThread 12  |  HWThread 13  |  HWThread 14  |  HWThread 15  |
++--------------------------------------+---------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+
+|           INSTR_RETIRED_ANY          |  FIXC0  | 1062384074352 | 1036415071743 | 1052600966172 | 1054789692902 | 1067560654541 | 1047140772969 | 1063377721926 | 1070395853218 | 1039607704349 | 1039203944144 | 1021167287992 | 1053327830351 | 1029103907737 | 1065359127658 | 1035985100198 | 1058987431644 |
+|         CPU_CLK_UNHALTED_CORE        |  FIXC1  |  962368120170 |  937971084671 |  951319896126 |  952615880874 |  962674274203 |  942721837444 |  958001948413 |  963713542269 |  941090357070 |  937104057117 |  924319305694 |  949240686047 |  928964172167 |  959757595020 |  933028997627 |  936503975787 |
+|         CPU_CLK_UNHALTED_REF         |  FIXC2  |  832954836298 |  812828889132 |  824416687198 |  825565709150 |  834282085390 |  816969631036 |  830207617928 |  835157406044 |  815491942954 |  812137083732 |  801021984958 |  822586472682 |  805038710476 |  831556644412 |  808526438616 |  811535268284 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE |   PMC0  |    3590712378 |    3346231008 |    3406454917 |    3453499476 |    3557858528 |    3403706927 |    3497341900 |    3419434893 |    3557593240 |    3426335665 |    3473419347 |    3509474601 |    3294336813 |    3550051922 |    3434509625 |    3434216267 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE |   PMC1  |    4898009353 |    4427932292 |    4503997056 |    4539411167 |    4682085388 |    4502844923 |    4601153705 |    4517165183 |    4576502676 |    4513198827 |    4522478313 |    4609811691 |    4314254144 |    4662173507 |    4508650566 |    4232780054 |
+|       SIMD_FP_256_PACKED_DOUBLE      |   PMC2  |    5395188163 |    5189979707 |    5280222115 |    5175091452 |    5270021723 |    5318743192 |    5209055517 |    5231177899 |    5066647603 |    5242994284 |    5184493447 |    5258492332 |    5032011937 |    5335969911 |    5365858948 |    5006897884 |
++--------------------------------------+---------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+
+
++-------------------------------------------+---------+----------------+---------------+---------------+--------------+
+|                   Event                   | Counter |       Sum      |      Min      |      Max      |      Avg     |
++-------------------------------------------+---------+----------------+---------------+---------------+--------------+
+|           INSTR_RETIRED_ANY STAT          |  FIXC0  | 16797407141896 | 1021167287992 | 1070395853218 | 1.049838e+12 |
+|         CPU_CLK_UNHALTED_CORE STAT        |  FIXC1  | 15141395730699 |  924319305694 |  963713542269 | 9.463372e+11 |
+|         CPU_CLK_UNHALTED_REF STAT         |  FIXC2  | 13120277408290 |  801021984958 |  835157406044 | 8.200173e+11 |
+| FP_COMP_OPS_EXE_SSE_FP_PACKED_DOUBLE STAT |   PMC0  |    55355177507 |    3294336813 |    3590712378 | 3.459699e+09 |
+| FP_COMP_OPS_EXE_SSE_FP_SCALAR_DOUBLE STAT |   PMC1  |    72612448845 |    4232780054 |    4898009353 | 4.538278e+09 |
+|       SIMD_FP_256_PACKED_DOUBLE STAT      |   PMC2  |    83562846114 |    5006897884 |    5395188163 | 5.222678e+09 |
++-------------------------------------------+---------+----------------+---------------+---------------+--------------+
+
++-------------------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|          Metric         | HWThread 0 | HWThread 1 | HWThread 2 | HWThread 3 | HWThread 4 | HWThread 5 | HWThread 6 | HWThread 7 | HWThread 8 | HWThread 9 | HWThread 10 | HWThread 11 | HWThread 12 | HWThread 13 | HWThread 14 | HWThread 15 |
++-------------------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+-------------+-------------+-------------+-------------+-------------+
+|   Runtime (RDTSC) [s]   |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |  1115.6544 |   1115.6544 |   1115.6544 |   1115.6544 |   1115.6544 |   1115.6544 |   1115.6544 |
+|   Runtime unhalted [s]  |   370.1459 |   360.7623 |   365.8965 |   366.3950 |   370.2637 |   362.5896 |   368.4666 |   370.6634 |   361.9621 |   360.4288 |    355.5116 |    365.0968 |    357.2981 |    369.1418 |    358.8615 |    360.1980 |
+|       Clock [MHz]       |  3003.9178 |  3000.2580 |  3000.1853 |  3000.0911 |  3000.0930 |  3000.1705 |  3000.1844 |  3000.1841 |  3000.4053 |  3000.0380 |   3000.1700 |   3000.2888 |   3000.2019 |   3000.8065 |   3000.3312 |   3000.3402 |
+|           CPI           |     0.9059 |     0.9050 |     0.9038 |     0.9031 |     0.9018 |     0.9003 |     0.9009 |     0.9003 |     0.9052 |     0.9018 |      0.9052 |      0.9012 |      0.9027 |      0.9009 |      0.9006 |      0.8843 |
+|       DP [MFLOP/s]      |    30.1708 |    28.5754 |    29.0751 |    28.8143 |    29.4696 |    29.2073 |    29.0700 |    28.9344 |    28.6453 |    28.9855 |     28.8685 |     29.2767 |     27.8141 |     29.6742 |     29.4366 |     27.9018 |
+|     AVX DP [MFLOP/s]    |    19.3436 |    18.6078 |    18.9314 |    18.5545 |    18.8948 |    19.0695 |    18.6762 |    18.7555 |    18.1657 |    18.7979 |     18.5882 |     18.8535 |     18.0415 |     19.1313 |     19.2384 |     17.9514 |
+|     Packed [MUOPS/s]    |     8.0544 |     7.6513 |     7.7862 |     7.7341 |     7.9127 |     7.8182 |     7.8038 |     7.7538 |     7.7302 |     7.7706 |      7.7604 |      7.8590 |      7.4632 |      7.9649 |      7.8881 |      7.5661 |
+|     Scalar [MUOPS/s]    |     4.3903 |     3.9689 |     4.0371 |     4.0688 |     4.1967 |     4.0361 |     4.1242 |     4.0489 |     4.1021 |     4.0453 |      4.0537 |      4.1319 |      3.8670 |      4.1789 |      4.0413 |      3.7940 |
+| Vectorization ratio [%] |    64.7217 |    65.8448 |    65.8547 |    65.5270 |    65.3435 |    65.9528 |    65.4245 |    65.6953 |    65.3315 |    65.7638 |     65.6878 |     65.5413 |     65.8699 |     65.5882 |     66.1233 |     66.6024 |
++-------------------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+-------------+-------------+-------------+-------------+-------------+
+
++------------------------------+------------+-----------+-----------+-----------+
+|            Metric            |     Sum    |    Min    |    Max    |    Avg    |
++------------------------------+------------+-----------+-----------+-----------+
+|   Runtime (RDTSC) [s] STAT   | 17850.4704 | 1115.6544 | 1115.6544 | 1115.6544 |
+|   Runtime unhalted [s] STAT  |  5823.6817 |  355.5116 |  370.6634 |  363.9801 |
+|       Clock [MHz] STAT       | 48007.6661 | 3000.0380 | 3003.9178 | 3000.4791 |
+|           CPI STAT           |    14.4230 |    0.8843 |    0.9059 |    0.9014 |
+|       DP [MFLOP/s] STAT      |   463.9196 |   27.8141 |   30.1708 |   28.9950 |
+|     AVX DP [MFLOP/s] STAT    |   299.6012 |   17.9514 |   19.3436 |   18.7251 |
+|     Packed [MUOPS/s] STAT    |   124.5170 |    7.4632 |    8.0544 |    7.7823 |
+|     Scalar [MUOPS/s] STAT    |    65.0852 |    3.7940 |    4.3903 |    4.0678 |
+| Vectorization ratio [%] STAT |  1050.8725 |   64.7217 |   66.6024 |   65.6795 |
++------------------------------+------------+-----------+-----------+-----------+
+```
+
+#### Comparison with previous results (`--enable-debug`) and with microbenchmarks
+
+---
+
+**Memory Metrics (`MEM` group, best rank):**
+
+| Metric                        | `--enable-debug` | `--enable-ipo` | Change                |
+|-------------------------------|------------------|----------------|-----------------------|
+| Runtime (RDTSC) [s]           | 306.15           | 1025.74        | ~3.35x increase       |
+| Memory BW [MB/s]              | 1515             | 264            | ~5.7x decrease        |
+| Memory Data Volume [GB]       | 464              | 270            | ~42% decrease         |
+| CPI                           | 0.84             | 0.90           | Slightly worse        |
+
+- **Observation:**  
+  - With IPO enabled, runtime increases and memory bandwidth drops significantly compared to the debug build.
+  - CPI is slightly worse, indicating less efficient instruction execution.
+  - Data volume is lower, possibly due to different run lengths or internal optimizations.
+
+---
+
+**Floating-Point Metrics (`FLOPS_DP` group, best rank):**
+
+| Metric           | `--enable-debug` | `--enable-ipo` | Change                |
+|------------------|------------------|----------------|-----------------------|
+| DP MFLOP/s       | ~77              | ~29            | ~2.7x decrease        |
+| Vectorization [%]| ~66              | ~66            | Similar               |
+| CPI              | ~0.84            | ~0.90          | Slightly worse        |
+
+- **Observation:**  
+  - Floating-point throughput drops with IPO enabled.
+  - Vectorization ratio remains similar.
+  - CPI is slightly worse.
+
+---
+
+**Comparison with Microbenchmarks:**
+
+| Metric         | SWIFT (`--enable-ipo`) | SWIFT (`--enable-debug`) | likwid-bench triad | likwid-bench peakflops |
+|----------------|-----------------------|-------------------------|--------------------|------------------------|
+| Memory BW      | 264 MB/s              | 1515 MB/s               | 26614 MB/s         | 2468 MB/s              |
+| DP MFLOP/s     | ~29                   | ~77                     | (not measured)     | 4936                   |
+| Vectorization  | ~66%                  | ~66%                    | (not measured)     | ~100%                  |
+| CPI            | 0.90                  | 0.84                    | 3.13 (triad)       | 8.43 (peakflops)       |
+
+- **Observation:**  
+  - Both SWIFT builds achieve only a small fraction of the hardware's peak memory bandwidth and floating-point throughput.
+  - IPO does not improve performance for this workload; in fact, it reduces both memory and compute throughput.
+  - Vectorization is much lower than in microbenchmarks.
+  - CPI is much better than microbenchmarks, but this is typical for memory-bound codes.
+
+---
+
+**Key Takeaways**
+
+- **IPO did not improve performance** for this configuration; memory and floating-point throughput are lower than with `--enable-debug`.
+- **SWIFT remains far from hardware peak** for both memory and compute, with vectorization much lower than microbenchmarks.
+- **Optimization potential remains high** for memory access patterns and vectorization in SWIFT.
+
 ---
 
 ## Intra (shared memory, threads)
@@ -900,4 +1016,4 @@ Group: 1
 
 ### Weak scaling
 
-### Load balancing
\ No newline at end of file
+### Load balancing

From 8a16c490ae11fe05f65ad524fd69bf00dea10fd0 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 30 May 2025 01:08:59 +0100
Subject: [PATCH 30/43] Forgotten bash script for generating HDF5 files (rivers
 and humans)

---
 examples/HumanMobility/gen.sh | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100755 examples/HumanMobility/gen.sh

diff --git a/examples/HumanMobility/gen.sh b/examples/HumanMobility/gen.sh
new file mode 100755
index 000000000..e6682d73c
--- /dev/null
+++ b/examples/HumanMobility/gen.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-rivers-3
+RIVERS=river-rivers-3
+DATA=data-rivers-3
+IMAGES=images-rivers-3
+
+# Remove previously generated data and images to regenerate the new ones
+rm -f ${HUMANS}.hdf5
+rm -f ${RIVERS}.hdf5
+rm -f ${DATA}/*
+rm -f ${IMAGES}/*
+
+# Generate acceleration field for river
+if [ ! -e ${RIVERS}.hdf5 ]
+then
+    echo "Generating acceleration field for the river..."
+    python3 makeRivers.py -b 100000 -g 10000 -f ${RIVERS}.hdf5
+fi
+
+# Generate the initial conditions if they are not present.
+if [ ! -e ${HUMANS}.hdf5 ]
+then
+    echo "Generating initial conditions for the human mobility box example..."
+    python3 makeIC.py -t gas -n 1000 -b 100000 -f ${HUMANS}.hdf5  # -t particles
+fi
\ No newline at end of file

From be89771a5ee52bbe740872c8341f462097a66486 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 30 May 2025 01:19:55 +0100
Subject: [PATCH 31/43] Add job scripts for perf analysis with likwid

---
 examples/HumanMobility/run-flops-dp.sh | 20 +++++++++++++++
 examples/HumanMobility/run-mem.sh      | 20 +++++++++++++++
 examples/HumanMobility/run-perf.sh     | 35 ++++++++++++++++++++++++++
 examples/HumanMobility/run.sh          |  8 +++---
 examples/HumanMobility/submit-perf.sh  |  6 +++++
 install_swift_cosma.sh                 | 10 +++++---
 6 files changed, 92 insertions(+), 7 deletions(-)
 create mode 100755 examples/HumanMobility/run-flops-dp.sh
 create mode 100755 examples/HumanMobility/run-mem.sh
 create mode 100755 examples/HumanMobility/run-perf.sh
 create mode 100755 examples/HumanMobility/submit-perf.sh

diff --git a/examples/HumanMobility/run-flops-dp.sh b/examples/HumanMobility/run-flops-dp.sh
new file mode 100755
index 000000000..8fbebd49b
--- /dev/null
+++ b/examples/HumanMobility/run-flops-dp.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-rivers-3
+RIVERS=river-rivers-3
+HUMANMOBILITY=humanMobility
+DATA=data-rivers-3
+IMAGES=images-rivers-3
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+# Run SWIFT with double-precision FLOPS measurement
+likwid-perfctr -f -C 0-15 -g FLOPS_DP -- \
+    swift_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+    -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+# likwid-mpirun -np ${SLURM_NTASKS:-1} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g FLOPS_DP -- \
+#     swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+#     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
diff --git a/examples/HumanMobility/run-mem.sh b/examples/HumanMobility/run-mem.sh
new file mode 100755
index 000000000..5a2df35b6
--- /dev/null
+++ b/examples/HumanMobility/run-mem.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-rivers-3
+RIVERS=river-rivers-3
+HUMANMOBILITY=humanMobility
+DATA=data-rivers-3
+IMAGES=images-rivers-3
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+# Run SWIFT with memory performance measurement
+likwid-perfctr -f -C 0-15 -g MEM -- \
+    swift_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+    -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+# likwid-mpirun -np ${SLURM_NTASKS:-1} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g MEM -- \
+#     swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+#     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
diff --git a/examples/HumanMobility/run-perf.sh b/examples/HumanMobility/run-perf.sh
new file mode 100755
index 000000000..92e36021a
--- /dev/null
+++ b/examples/HumanMobility/run-perf.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+#SBATCH --ntasks=1             # Total number of MPI tasks (cores) (max 16)
+#SBATCH --nodes=1              # Number of nodes
+#SBATCH --ntasks-per-node=1    # MPI tasks per node (max 16)
+#SBATCH --cpus-per-task=16     # CPU cores per MPI rank
+#SBATCH --mem=120G             # Memory per node
+#SBATCH -p cosma               # COSMA5 partition
+#SBATCH -A durham              # Account
+#SBATCH -t 2-00:00:00
+#SBATCH --mail-type=END
+#SBATCH --mail-user=lcgk69@durham.ac.uk
+#SBATCH --job-name=hm-perf-both
+#SBATCH --output=hm-perf-both.out
+#SBATCH --error=hm-perf-both.err
+
+# Load required modules
+module purge
+module load cosma
+module load intel_comp/2025.0.1
+module load umf compiler-rt tbb compiler mpi
+module load python
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+module load likwid/5.4.1
+
+module list
+
+echo "=== Running MEM benchmark ==="
+./run-mem.sh
+
+echo "=== Running FLOPS_DP benchmark ==="
+./run-flops-dp.sh
\ No newline at end of file
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index aafb8e61b..de2c34821 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -36,9 +36,11 @@ envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
 # mpirun -np ${SLURM_NTASKS:-8} \
 #     swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
 #     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
-# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 100 ${HUMANMOBILITY}.yml
+# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 100 ${HUMANMOBILITY}.yml
 likwid-perfctr -f -C 0 -g MEM swift_intel2025 -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
-# likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=16 -n 1000 ${HUMANMOBILITY}.yml
+# likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
 # swift_intel2025 -h | grep version
-# likwid-mpirun -np 8 -t 16 -omp intel -g MEM -- swift_mpi --threads=16 -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
+# likwid-mpirun -np ${SLURM_NTASKS:-1} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g MEM -- \ # FLOPS_DP
+    # swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
+    # -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
 # likwid-perfctr -a
diff --git a/examples/HumanMobility/submit-perf.sh b/examples/HumanMobility/submit-perf.sh
new file mode 100755
index 000000000..64e53f831
--- /dev/null
+++ b/examples/HumanMobility/submit-perf.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+sbatch --job-name="hm-perf-both" \
+       --output="hm-perf-both.out" \
+       --error="hm-perf-both.err" \
+       run-perf.sh
\ No newline at end of file
diff --git a/install_swift_cosma.sh b/install_swift_cosma.sh
index 42ef4761a..4b66100ea 100755
--- a/install_swift_cosma.sh
+++ b/install_swift_cosma.sh
@@ -5,9 +5,9 @@
 #SBATCH --cpus-per-task=16        # Use all cores for parallel make
 #SBATCH --mem=64G                 # Memory for compilation
 #SBATCH -J sw-build              # Job name
-#SBATCH -o sw.out               # Output file
-#SBATCH -e sw.err               # Error file
-#SBATCH -p cosma               # COSMA5 partition
+##SBATCH -o sw.out               # Output file
+##SBATCH -e sw.err               # Error file
+#SBATCH -p cosma8-shm               # COSMA5 partition
 #SBATCH -A durham               # Account
 #SBATCH -t 0:30:00             # Increased time for compilation
 
@@ -37,6 +37,8 @@ export AR=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-a
 export LD=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-link
 export RANLIB=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ranlib
 
+module list
+
 # The main installation script for SWIFT_ABM
 
 echo "############################"
@@ -51,7 +53,7 @@ echo "#############################"
 #export CFLAGS="-fsanitize=address -g"
 ./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
     --program-suffix=_intel2025 \
-    CFLAGS="-Wno-error=gnu-folding-constant" \
+    CFLAGS="-Wno-error=gnu-folding-constant -g" \
     LDFLAGS= \
     --enable-ipo \
     --enable-mpi \

From 7a70e098b57ac9e1b796996de4c9bdc8a5358267 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 30 May 2025 01:26:34 +0100
Subject: [PATCH 32/43] Add job scripts for perf analysis with Maqao

---
 examples/HumanMobility/humanMobility.yml |  6 ++---
 examples/HumanMobility/job.sh            | 16 ++++++-------
 examples/HumanMobility/run_maqao.sh      | 30 ++++++++++++++++++++++++
 3 files changed, 41 insertions(+), 11 deletions(-)
 create mode 100755 examples/HumanMobility/run_maqao.sh

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
index 41d3a1663..2e1fbff26 100644
--- a/examples/HumanMobility/humanMobility.yml
+++ b/examples/HumanMobility/humanMobility.yml
@@ -15,7 +15,7 @@ TimeIntegration:
 
 # Parameters governing the snapshots
 Snapshots:
-  subdir:              data-rivers-3
+  subdir:              data-maqao
   basename:            humanMobility # Common part of the name of output files
   time_first:          0.         # Time of the first output (in internal units)
   delta_time:          1e-2 #1440        # Time difference between consecutive outputs (in internal units)
@@ -36,7 +36,7 @@ Scheduler:
 
 # Parameters related to the initial conditions
 InitialConditions:
-  file_name:  humans-rivers-3.hdf5     # The file to read
+  file_name:  humans-maqao.hdf5     # The file to read
   periodic:   1
 
 PhysicalConstants:
@@ -72,4 +72,4 @@ Gravity:
 RiverPotential:
 #  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
 #  mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  river-rivers-3.hdf5 # Path to acceleration field HDF5 file
+  parameter_file:  river-maqao.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index 20506f4e2..cdeef0a57 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -1,13 +1,13 @@
 #!/bin/bash
 
-#SBATCH --ntasks=1             # Total number of MPI tasks (cores) (max 16)
+#SBATCH --ntasks=4              # Total number of MPI tasks (cores) (max 16)
 #SBATCH --nodes=1               # Number of nodes
-#SBATCH --ntasks-per-node=1    # MPI tasks per node (max 16)
-#SBATCH --cpus-per-task=16      # CPU cores per MPI rank
-#SBATCH --mem=120G               # Memory per node
-#SBATCH -p cosma               # COSMA5 partition
+#SBATCH --ntasks-per-node=4     # MPI tasks per node (max 16)
+#SBATCH --cpus-per-task=4      # CPU cores per MPI rank
+#SBATCH --mem=64G               # Memory per node
+#SBATCH -p cosma8-shm           # COSMA5 partition
 #SBATCH -A durham               # Account
-#SBATCH -t 2-00:00:00
+#SBATCH -t 12:00:00             # Time limit hrs:min:sec
 #SBATCH --mail-type=END
 #SBATCH --mail-user=lcgk69@durham.ac.uk
 
@@ -40,7 +40,7 @@ module load gsl
 module load parmetis/4.0.3-64bit
 module load parallel_hdf5/1.14.4
 module load sundials/5.8.0_c8_single
-module load likwid/5.4.1
+module load maqao #likwid/5.4.1
 
 
 # Additional module for visualisation
@@ -56,7 +56,7 @@ case $mode in
         ./gen.sh
         ;;
     --run)
-        ./run.sh
+        ./run_maqao.sh
         ;;
     --vis)
         ./visualise.sh
diff --git a/examples/HumanMobility/run_maqao.sh b/examples/HumanMobility/run_maqao.sh
new file mode 100755
index 000000000..ce37e974a
--- /dev/null
+++ b/examples/HumanMobility/run_maqao.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-maqao
+RIVERS=river-maqao
+HUMANMOBILITY=humanMobility
+DATA=data-maqao
+IMAGES=images-maqao
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+#OMP_NUM_THREADS=4 mpirun -n 4 bin/bt-mz.B.x
+    # --number-processes=${SLURM_NTASKS:-8} \
+    # --envv_OMP_NUM_THREADS=4 -- bin/bt-mz.B.x
+#maqao oneview -R1 --mpi-command="mpirun -np ${SLURM_NTASKS:-8}" \
+
+# Run Maqao oneview analysis on SWIFT (single node, threaded)
+# maqao oneview -R1 --output-format=all -- \
+#     ${SWIFT} -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
+
+# Run Maqao oneview analysis on SWIFT MPI version with correct options
+maqao oneview -R1 --output-format=all \
+    --mpi-command="mpirun -np ${SLURM_NTASKS:-4}" \
+    --envv_OMP_NUM_THREADS="${SLURM_CPUS_PER_TASK:-4}" -- \
+    ${SWIFT_MPI} -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-4} -n 1000 ${HUMANMOBILITY}.yml
\ No newline at end of file

From 69c17dabca80346717ef80fddf357a1c2db06d89 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 30 May 2025 08:57:15 +0100
Subject: [PATCH 33/43] Rename for perf analysis with likwid

---
 examples/HumanMobility/{run-perf.sh => run_likwid.sh} | 0
 examples/HumanMobility/submit-perf.sh                 | 8 ++++----
 2 files changed, 4 insertions(+), 4 deletions(-)
 rename examples/HumanMobility/{run-perf.sh => run_likwid.sh} (100%)

diff --git a/examples/HumanMobility/run-perf.sh b/examples/HumanMobility/run_likwid.sh
similarity index 100%
rename from examples/HumanMobility/run-perf.sh
rename to examples/HumanMobility/run_likwid.sh
diff --git a/examples/HumanMobility/submit-perf.sh b/examples/HumanMobility/submit-perf.sh
index 64e53f831..9e9c5ace4 100755
--- a/examples/HumanMobility/submit-perf.sh
+++ b/examples/HumanMobility/submit-perf.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-sbatch --job-name="hm-perf-both" \
-       --output="hm-perf-both.out" \
-       --error="hm-perf-both.err" \
-       run-perf.sh
\ No newline at end of file
+sbatch --job-name="hm_likwid" \
+       --output="hm_likwid.out" \
+       --error="hm_likwid.err" \
+       run_likwid.sh
\ No newline at end of file

From ae6a482469b8e57332c3575fea4f97c90c27f551 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 6 Jun 2025 16:29:28 +0100
Subject: [PATCH 34/43] Extend documentation about performance analysis

---
 examples/HumanMobility/perf/job_scheduling.md | 181 ++++++++++++++++++
 examples/HumanMobility/perf/paws.md           |  85 ++++++--
 2 files changed, 249 insertions(+), 17 deletions(-)
 create mode 100644 examples/HumanMobility/perf/job_scheduling.md

diff --git a/examples/HumanMobility/perf/job_scheduling.md b/examples/HumanMobility/perf/job_scheduling.md
new file mode 100644
index 000000000..3fbabeef9
--- /dev/null
+++ b/examples/HumanMobility/perf/job_scheduling.md
@@ -0,0 +1,181 @@
+# A SLURM job scheduling suite
+
+### Scripts
+
+Whenever you want to submit a job for a simulation (without or with profiling measurement) or for other applications (like visualisation), use the provided job submission script.
+
+* `./submit.sh --${type} -N ${nodes} -n ${ntasks} -c ${threads}` .. a job submission script to submit a job of choice with possible types to select from: `gen` .. to generate data of humans and a map of rivers; `vis` .. to plot simulation results; `map` .. to plot only geography without humans; a set of other options to run a simulation (`run` .. without proifiling; `likwid` .. profiled with _likwid_; `maqao` .. profiled with _Maqao_; `vtune` .. profiled with _Intel VTune_; `advisor` .. profiled with _Intel Advisor_; `inspector` .. profiled with _Intel Inspector_; `scorep` .. profiled with _Score-P_)
+
+* `job.sh` .. a selector script to pass SLURM parameters further down to specific simulation scripts
+
+* `gen.sh` .. a script to generate data of humans and a map of rivers (no parameters are passed)
+
+* `visualise.sh` .. a script to plot simulation results (no parameters are passed)
+
+* `map.sh` .. a script to plot only geography without humans (no parameters are passed)
+
+* `run.sh` .. a simulation script without profiling (with automatic selection of call mode between serial and parallel determined from additional parameters passed in `submit.sh`)
+
+* `run_likwid.sh` .. a simulation script with profiling by means of _likwid_
+
+* `run_maqao.sh` .. a simulation script with profiling by means of _Maqao_
+
+* `run_vtune.sh` .. a simulation script with profiling by means of _Intel VTune_
+
+* `run_advisor.sh` .. a simulation script with profiling by means of _Intel Advisor_
+
+* `run_inspector.sh` .. a simulation script with profiling by means of _Intel Inspector_
+
+* `run_scorep.sh` .. a simulation script with profiling by means of _Score-P_
+
+### Usage examples
+
+Basic usage with defaults (1 node, 1 MPI rank, 64 threads):
+```bash
+./submit.sh --run
+./submit.sh --maqao
+./submit.sh --vtune
+```
+
+With custom resource allocation:
+```bash
+./submit.sh --likwid -N 1 -n 1 -c 32     # 1 node, 1 MPI rank, 32 threads
+./submit.sh --advisor -N 1 -n 1 -c 16    # 1 node, 1 MPI rank, 16 threads
+./submit.sh --inspector -N 1 -n 1 -c 8   # 1 node, 1 MPI rank, 8 threads
+./submit.sh --scorep -N 1 -n 4 -c 8      # 1 node, 4 MPI ranks, 8 threads
+```
+
+### Performance analysis
+
+For strong scaling, I begin with a minimal simulation of human mobility on a square 100 x 100 humans on 10 x 10 km (for minimum of either 1000 steps or 10 minutes). For weak scaling, I'd like to begin on 2 ranks from a square 100 x 100 humans on 10 x 10 km and then on 8 and 18 ranks, increasing the problem size accordingly.
+
+To conduct performance analysis, we have the following queues:
+
+* The _cosma5_ queue is comprised of the new COSMA5 nodes, a total of 3 nodes each with 256 cores and 1.5TB RAM
+
+* The _cosma_ queue is comprised of the old COSMA nodes, a total of ~160 nodes each with 16 cores and 126GB RAM
+
+#### Strong scaling
+
+**Intel Advisor Analysis for Strong Scaling**
+
+For strong scaling analysis with Intel Advisor, we keep the problem size constant (100 x 100 humans on 10 x 10 km) and vary the number of computational resources:
+
+**Base Configuration (Serial Baseline):**
+```bash
+./submit.sh --advisor -N 1 -n 1 -c 1     # 1 node, 1 MPI rank, 1 thread (serial)
+```
+
+**Intra-node Scaling (COSMA5 queue - threading analysis):**
+```bash
+# Test threading efficiency within a single node
+./submit.sh --advisor -N 1 -n 1 -c 16    # 1 node, 1 MPI rank, 16 threads
+./submit.sh --advisor -N 1 -n 1 -c 32    # 1 node, 1 MPI rank, 32 threads  
+./submit.sh --advisor -N 1 -n 1 -c 64    # 1 node, 1 MPI rank, 64 threads
+./submit.sh --advisor -N 1 -n 1 -c 128   # 1 node, 1 MPI rank, 128 threads
+./submit.sh --advisor -N 1 -n 1 -c 256   # 1 node, 1 MPI rank, 256 threads (max COSMA5)
+```
+
+**Inter-node Scaling (COSMA5 queue - MPI analysis):**
+```bash
+# Test MPI scaling across multiple nodes with fixed threads per rank
+./submit.sh --advisor -N 2 -n 2 -c 128   # 2 nodes, 2 MPI ranks, 128 threads each
+./submit.sh --advisor -N 3 -n 3 -c 85    # 3 nodes, 3 MPI ranks, ~85 threads each
+```
+
+**COSMA queue comparison:**
+```bash
+# Compare with older COSMA hardware
+./submit.sh --advisor -N 1 -n 1 -c 16    # 1 old COSMA node, 1 MPI rank, 16 threads (max)
+./submit.sh --advisor -N 2 -n 2 -c 8     # 2 old COSMA nodes, 2 MPI ranks, 8 threads each
+./submit.sh --advisor -N 4 -n 4 -c 4     # 4 old COSMA nodes, 4 MPI ranks, 4 threads each
+```
+
+**Intel Advisor Analysis Focus:**
+- **Survey Analysis**: Identify hotspots and vectorization opportunities
+- **Tripcounts Analysis**: Analyze loop characteristics and iteration counts
+- **Map Analysis**: Detailed vectorization recommendations
+- **Dependencies Analysis**: Identify loop-carried dependencies preventing vectorization
+- **Memory Access Patterns**: Understand cache efficiency and memory bottlenecks
+
+#### Weak scaling
+
+**Intel Advisor Analysis for Weak Scaling**
+
+For weak scaling, we maintain constant work per computational unit by increasing problem size proportionally with resources:
+
+**Baseline (2 MPI ranks, 100 x 100 humans):**
+```bash
+./submit.sh --advisor -N 1 -n 2 -c 8     # Base: 100x100 humans, 2 ranks, 8 threads each
+```
+
+**Scale to 8 MPI ranks (200 x 200 humans):**
+```bash
+# COSMA5 queue (high core count nodes)
+./submit.sh --advisor -N 1 -n 8 -c 32    # 1 node, 8 ranks, 32 threads each (256 total cores)
+
+# COSMA queue (distributed across multiple nodes)
+./submit.sh --advisor -N 2 -n 8 -c 2     # 2 nodes, 8 ranks, 2 threads each
+./submit.sh --advisor -N 4 -n 8 -c 2     # 4 nodes, 8 ranks, 2 threads each  
+./submit.sh --advisor -N 8 -n 8 -c 2     # 8 nodes, 8 ranks, 2 threads each
+```
+
+**Scale to 18 MPI ranks (300 x 300 humans):**
+```bash
+# COSMA5 queue (maximum utilization)
+./submit.sh --advisor -N 1 -n 18 -c 14   # 1 node, 18 ranks, ~14 threads each
+
+# COSMA queue (distributed)
+./submit.sh --advisor -N 2 -n 18 -c 1    # 2 nodes, 18 ranks, 1 thread each
+./submit.sh --advisor -N 9 -n 18 -c 1    # 9 nodes, 18 ranks, 1 thread each
+./submit.sh --advisor -N 18 -n 18 -c 1   # 18 nodes, 18 ranks, 1 thread each
+```
+
+**Intel Advisor Weak Scaling Analysis:**
+- **Memory Bandwidth Scaling**: Track memory access patterns as problem size increases
+- **Cache Efficiency**: Monitor cache hit rates with larger datasets
+- **Vectorization Efficiency**: Ensure vectorization remains effective with scaling
+- **Load Balancing**: Identify task distribution imbalances across ranks
+- **Communication Overhead**: Analyze MPI communication costs relative to computation
+
+#### Intel Advisor Configuration Details
+
+**Environment Setup for Advisor Analysis:**
+```bash
+# Advisor-specific environment variables (set in run_advisor.sh)
+export ADVIXE_EXPERIMENTAL=roofline
+export ADVIXE_RUNTOOL_OPTIONS="--enable-stack-stitching --enable-data-transfer-analysis"
+```
+
+**Analysis Workflow:**
+1. **Survey Collection**: Identify performance bottlenecks and hotspots
+2. **Tripcounts Collection**: Gather loop iteration information
+3. **Map Collection**: Analyze vectorization opportunities  
+4. **Dependencies Collection**: Identify optimization blockers
+5. **Roofline Analysis**: Compare achieved vs. theoretical performance
+
+**Output Analysis:**
+- **Threading Efficiency**: Compare single-threaded vs. multi-threaded performance
+- **Vectorization Ratio**: Percentage of vectorized vs. scalar operations
+- **Memory Bound vs. Compute Bound**: Identify limiting factors
+- **Scaling Efficiency**: Calculate parallel efficiency = (T₁ / (N × Tₙ)) × 100%
+- **Communication vs. Computation**: MPI overhead analysis
+
+**Expected Deliverables:**
+- Performance scaling curves for both strong and weak scaling
+- Vectorization efficiency reports across different configurations
+- Memory bandwidth utilization analysis
+- Task-based parallelism efficiency metrics
+- Recommendations for optimal resource allocation
+
+#### Analysis Integration with SWIFT's Task-Based Architecture
+
+Given SWIFT's task-based parallelism model, Intel Advisor will specifically analyze:
+
+- **Task Scheduler Efficiency**: How well SWIFT's scheduler distributes tasks across available cores
+- **Task Granularity**: Optimal task sizes for vectorization and cache efficiency  
+- **Memory Access Patterns**: Cache reuse between dependent tasks
+- **Load Balancing**: Work distribution across MPI ranks and OpenMP threads
+- **Hybrid Parallelism**: Effectiveness of MPI + OpenMP combination
+
+This comprehensive analysis will provide insights into SWIFT's performance characteristics on both traditional (COSMA) and high-core-count (COSMA5) architectures, guiding optimization strategies for large-scale human mobility simulations.
\ No newline at end of file
diff --git a/examples/HumanMobility/perf/paws.md b/examples/HumanMobility/perf/paws.md
index e1d759067..ae5ea6934 100644
--- a/examples/HumanMobility/perf/paws.md
+++ b/examples/HumanMobility/perf/paws.md
@@ -954,12 +954,13 @@ Group 1: FLOPS_DP
 
 **Memory Metrics (`MEM` group, best rank):**
 
-| Metric                        | `--enable-debug` | `--enable-ipo` | Change                |
-|-------------------------------|------------------|----------------|-----------------------|
-| Runtime (RDTSC) [s]           | 306.15           | 1025.74        | ~3.35x increase       |
-| Memory BW [MB/s]              | 1515             | 264            | ~5.7x decrease        |
-| Memory Data Volume [GB]       | 464              | 270            | ~42% decrease         |
-| CPI                           | 0.84             | 0.90           | Slightly worse        |
+| Metric                        | `--enable-debug`              | `--enable-ipo`   | Change                |
+|                               | (4 MPI ranks × 4 OMP threads) | (16 OMP threads) |                       |
+|-------------------------------|-------------------------------|------------------|-----------------------|
+| Runtime (RDTSC) [s]           | 306.15                        | 1025.74          | ~3.35x increase       |
+| Memory BW [MB/s]              | 1515                          | 264              | ~5.7x decrease        |
+| Memory Data Volume [GB]       | 464                           | 270              | ~42% decrease         |
+| CPI                           | 0.84                          | 0.90             | Slightly worse        |
 
 - **Observation:**  
   - With IPO enabled, runtime increases and memory bandwidth drops significantly compared to the debug build.
@@ -970,11 +971,12 @@ Group 1: FLOPS_DP
 
 **Floating-Point Metrics (`FLOPS_DP` group, best rank):**
 
-| Metric           | `--enable-debug` | `--enable-ipo` | Change                |
-|------------------|------------------|----------------|-----------------------|
-| DP MFLOP/s       | ~77              | ~29            | ~2.7x decrease        |
-| Vectorization [%]| ~66              | ~66            | Similar               |
-| CPI              | ~0.84            | ~0.90          | Slightly worse        |
+| Metric           | SWIFT(`--enable-debug`)       | SWIFT(`--enable-ipo`) | Change                |
+|                  | (4 MPI ranks × 4 OMP threads) | (16 OMP threads)      |                       |
+|------------------|-------------------------------|-----------------------|-----------------------|
+| DP MFLOP/s       | ~77                           | ~29                   | ~2.7x decrease        |
+| Vectorization [%]| ~66                           | ~66                   | Similar               |
+| CPI              | ~0.84                         | ~0.90                 | Slightly worse        |
 
 - **Observation:**  
   - Floating-point throughput drops with IPO enabled.
@@ -985,12 +987,13 @@ Group 1: FLOPS_DP
 
 **Comparison with Microbenchmarks:**
 
-| Metric         | SWIFT (`--enable-ipo`) | SWIFT (`--enable-debug`) | likwid-bench triad | likwid-bench peakflops |
-|----------------|-----------------------|-------------------------|--------------------|------------------------|
-| Memory BW      | 264 MB/s              | 1515 MB/s               | 26614 MB/s         | 2468 MB/s              |
-| DP MFLOP/s     | ~29                   | ~77                     | (not measured)     | 4936                   |
-| Vectorization  | ~66%                  | ~66%                    | (not measured)     | ~100%                  |
-| CPI            | 0.90                  | 0.84                    | 3.13 (triad)       | 8.43 (peakflops)       |
+| Metric         | SWIFT(`--enable-ipo`) | SWIFT(`--enable-debug`)       | likwid-bench triad | likwid-bench peakflops |
+|                | (16 OMP threads)      | (4 MPI ranks × 4 OMP threads) |                    |                        |
+|----------------|-----------------------|-------------------------------|--------------------|------------------------|
+| Memory BW      | 264 MB/s              | 1515 MB/s                     | 26614 MB/s         | 2468 MB/s              |
+| DP MFLOP/s     | ~29                   | ~77                           | (not measured)     | 4936                   |
+| Vectorization  | ~66%                  | ~66%                          | (not measured)     | ~100%                  |
+| CPI            | 0.90                  | 0.84                          | 3.13 (triad)       | 8.43 (peakflops)       |
 
 - **Observation:**  
   - Both SWIFT builds achieve only a small fraction of the hardware's peak memory bandwidth and floating-point throughput.
@@ -1008,6 +1011,54 @@ Group 1: FLOPS_DP
 
 ---
 
+### MAQAO Performance Analysis
+
+#### Runtime Comparison
+
+| Tool         | Configuration                                  | Runtime [s] | Notes                   |
+|--------------|------------------------------------------------|-------------|-------------------------|
+| **MAQAO**    | (4 MPI ranks × 4 OMP threads)                  | 294.31      | 1000 humans, 1000 steps |
+| **LIKWID**   | `--enable-debug` (4 MPI ranks × 4 OMP threads) | 306.15      | 1000 humans, 100 steps  |
+| **LIKWID**   | `--enable-ipo` (16 OMP threads)                | 1025.74     | 1000 humans, 100 steps  |
+
+#### MAQAO Analysis Results
+
+- **Total Time**: 294.31 seconds
+- **Profiled Time**: 239.35 seconds (81.3% coverage)
+- **Configuration**: 4 MPI ranks with 4 OpenMP threads each
+
+#### Key Differences in Methodology
+
+| Aspect              | LIKWID                           | MAQAO                            |
+|---------------------|----------------------------------|----------------------------------|
+| **Scope**           | Hardware counter-based profiling | Static + dynamic analysis        |
+| **Granularity**     | Thread/rank level                | Loop level                       |
+| **Memory Analysis** | Aggregate bandwidth measurements | Detailed access pattern analysis |
+| **Vectorization**   | Overall percentage               | Per-loop analysis                |
+| **Output**          | Performance counters             | Interactive HTML reports         |
+
+#### Performance Insights
+
+1. **Runtime Efficiency**
+  - **MAQAO MPI run (294s)** vs **LIKWID debug (306s)**: Similar performance
+2. **Analysis Depth**
+  - **LIKWID** provides quantitative metrics (1515 MB/s memory BW, ~77 MFLOP/s)
+  - **MAQAO** provides qualitative loop-by-loop optimization guidance
+3. **Profiling Coverage**
+  - **MAQAO** achieved 81.3% profiling coverage
+  - **LIKWID** measures specific hardware events with 100% coverage
+4. **Memory Access Patterns** - MAQAO shows various loops with different memory access patterns: 
+  - Some loops show spans of 652,539-171,205 bytes
+  - Memory access patterns vary from simple (6 loads) to complex (18 mixed loads/stores)
+
+#### Complementary Analysis
+
+The tools complement each other:
+- **LIKWID** gives you the **"what"** (quantitative performance)
+- **MAQAO** gives you the **"where and why"** (code locations and optimization opportunities)
+
+---
+
 ## Intra (shared memory, threads)
 
 ## Inter (MPI)

From b05d02d49f84e5365b6641fa5c9fb25f69564780 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Tue, 10 Jun 2025 19:46:17 +0100
Subject: [PATCH 35/43] Add performance analysis visualisation script

---
 .../data-rivers-1-2-32/pa_vis.py              | 679 ++++++++++++++++++
 1 file changed, 679 insertions(+)
 create mode 100644 examples/HumanMobility/data-rivers-1-2-32/pa_vis.py

diff --git a/examples/HumanMobility/data-rivers-1-2-32/pa_vis.py b/examples/HumanMobility/data-rivers-1-2-32/pa_vis.py
new file mode 100644
index 000000000..5eebdde61
--- /dev/null
+++ b/examples/HumanMobility/data-rivers-1-2-32/pa_vis.py
@@ -0,0 +1,679 @@
+#!/usr/bin/env python3
+"""
+Performance Analysis Visualization (pa_vis.py)
+
+Creates a two-page PDF:
+- Page 1: Memory Usage and CPU Usage across MPI ranks
+- Page 2: Thread Usage with bars for every individual thread
+
+Usage:
+    python pa_vis.py [--use-balance-logs] [--use-dat-files] [--use-thread-files]
+    
+By default, tries to use balance logs first, then falls back to .dat files.
+"""
+
+import numpy as np
+import sys
+import os
+import glob
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import argparse
+
+def parse_balance_logs():
+    """Parse rank_memory_balance.log and rank_cpu_balance.log files"""
+    memfile = "rank_memory_balance.log"
+    cpufile = "rank_cpu_balance.log"
+    
+    if not (os.path.exists(memfile) and os.path.exists(cpufile)):
+        return None, None
+    
+    print(f"Using balance log files: {memfile}, {cpufile}")
+    
+    # Load memory data
+    memdat = np.loadtxt(
+        memfile,
+        dtype=[("step", int), ("rank", int), ("resident", float)],
+    )
+    
+    # Load CPU data  
+    cpudat = np.loadtxt(
+        cpufile,
+        dtype=[("step", int), ("rank", int), ("user", float), 
+               ("sys", float), ("sum", float), ("deadfrac", float)],
+    )
+    
+    return memdat, cpudat
+
+def parse_thread_info_files():
+    """Parse thread_info_MPI-step*.dat files for thread usage analysis"""
+    thread_files = glob.glob("thread_info_MPI-step*.dat")
+    
+    if not thread_files:
+        print("No thread_info_MPI files found")
+        return None
+    
+    print(f"Using thread info files: {len(thread_files)} files found")
+    
+    # Parse thread timing data
+    thread_data = {}  # rank -> {step -> {thread_id -> total_time}}
+    rank_thread_counts = {}  # rank -> max_threads
+    
+    for thread_file in sorted(thread_files):
+        # Extract step from filename: thread_info_MPI-step1.dat
+        step_part = thread_file.split('-step')[1].split('.dat')[0]
+        step = int(step_part)
+        
+        try:
+            # Load thread data - format from SWIFT source code analysis
+            data = np.loadtxt(thread_file)
+            
+            if data.size == 0:
+                continue
+                
+            # First row contains metadata: rank, tic_step, toc_step, etc.
+            if len(data.shape) == 1:
+                # Single row file
+                continue
+                
+            # Skip first row (metadata) and process task data
+            task_data = data[1:] if data.shape[0] > 1 else data
+            
+            if task_data.size == 0:
+                continue
+            
+            # Process each task line
+            # Format: rank rid type subtype pair tic toc ci.hydro.count cj.hydro.count ci.grav.count cj.grav.count flags sid
+            if len(task_data.shape) == 2:
+                for row in task_data:
+                    if len(row) >= 7:  # Ensure we have enough columns
+                        rank = int(row[0])  # First column is rank
+                        thread_id = int(row[1])  # rid = runner/thread ID
+                        tic = int(row[5])
+                        toc = int(row[6])
+                        
+                        if toc > tic:  # Valid task timing
+                            task_time = toc - tic  # Time in ticks
+                            
+                            # Initialize rank data structure
+                            if rank not in thread_data:
+                                thread_data[rank] = {}
+                                rank_thread_counts[rank] = 0
+                            
+                            if step not in thread_data[rank]:
+                                thread_data[rank][step] = {}
+                            
+                            if thread_id not in thread_data[rank][step]:
+                                thread_data[rank][step][thread_id] = 0
+                            
+                            thread_data[rank][step][thread_id] += task_time
+                            rank_thread_counts[rank] = max(rank_thread_counts[rank], thread_id + 1)
+            
+        except Exception as e:
+            print(f"Warning: Could not parse {thread_file}: {e}")
+            continue
+    
+    if not thread_data:
+        return None
+    
+    return thread_data, rank_thread_counts
+
+def parse_dat_files():
+    """Parse memuse_report-rank*-step*.dat and mpiuse_report-rank*-step*.dat files"""
+    memfiles = glob.glob("memuse_report-rank*-step*.dat")
+    cpufiles = glob.glob("mpiuse_report-rank*-step*.dat")  # MPI communication files
+    
+    if not memfiles:
+        print("No memuse_report files found")
+        return None, None
+    
+    print(f"Using .dat files: {len(memfiles)} memuse files, {len(cpufiles)} mpiuse files")
+    
+    # Parse memory usage files
+    mem_data = []
+    for memfile in sorted(memfiles):
+        # Extract rank and step from filename: memuse_report-rank0-step1.dat
+        parts = os.path.basename(memfile).replace('.dat', '').split('-')
+        rank = int(parts[1].replace('rank', ''))
+        step = int(parts[2].replace('step', ''))
+        
+        try:
+            # Load memory data - format varies, so we need to be careful
+            with open(memfile, 'r') as f:
+                lines = f.readlines()
+                
+            # Find peak memory usage from comments or data
+            peak_mem = 0
+            for line in lines:
+                if line.startswith('# Peak memory usage'):
+                    # Extract MB value and convert to KB
+                    peak_mb = float(line.split(':')[1].strip().split()[0])
+                    peak_mem = peak_mb * 1024  # Convert MB to KB
+                    break
+            
+            if peak_mem == 0:
+                # Try to get from process memory line
+                for line in lines:
+                    if 'Memory use by process' in line:
+                        # Try to extract memory value - this is system dependent
+                        try:
+                            parts = line.split()
+                            for i, part in enumerate(parts):
+                                if 'MB' in part or 'KB' in part:
+                                    val = float(parts[i-1])
+                                    if 'MB' in part:
+                                        peak_mem = val * 1024
+                                    else:
+                                        peak_mem = val
+                                    break
+                        except:
+                            peak_mem = 1000000  # Default 1GB in KB
+                        break
+            
+            if peak_mem == 0:
+                peak_mem = 1000000  # Default 1GB in KB
+                
+            mem_data.append((step, rank, peak_mem))
+            
+        except Exception as e:
+            print(f"Warning: Could not parse {memfile}: {e}")
+            continue
+    
+    # Parse MPI/CPU usage files (these represent communication time, not total CPU)
+    cpu_data = []
+    for cpufile in sorted(cpufiles):
+        # Extract rank and step from filename
+        parts = os.path.basename(cpufile).replace('.dat', '').split('-')
+        rank = int(parts[1].replace('rank', ''))
+        step = int(parts[2].replace('step', ''))
+        
+        try:
+            # Load MPI communication data
+            with open(cpufile, 'r') as f:
+                lines = f.readlines()
+            
+            # Sum up communication times as a proxy for CPU usage
+            total_comm_time = 0
+            for line in lines:
+                if line.startswith('#') or not line.strip():
+                    continue
+                try:
+                    # Format: stic etic dtic step rank otherrank type itype subtype isubtype activation tag size sum
+                    parts = line.split()
+                    if len(parts) >= 3:
+                        dtic = float(parts[2])  # Duration of MPI operation
+                        total_comm_time += dtic
+                except:
+                    continue
+            
+            # Convert ticks to milliseconds (approximate)
+            cpu_time_ms = total_comm_time / 1000.0  # Rough conversion
+            cpu_data.append((step, rank, 0, 0, cpu_time_ms, 0))  # user, sys, sum, deadfrac
+            
+        except Exception as e:
+            print(f"Warning: Could not parse {cpufile}: {e}")
+            continue
+    
+    if not mem_data and not cpu_data:
+        return None, None
+    
+    # Convert to numpy arrays with same format as balance logs
+    if mem_data:
+        memdat = np.array(mem_data, dtype=[("step", int), ("rank", int), ("resident", float)])
+    else:
+        memdat = None
+        
+    if cpu_data:
+        cpudat = np.array(cpu_data, dtype=[
+            ("step", int), ("rank", int), ("user", float), 
+            ("sys", float), ("sum", float), ("deadfrac", float)
+        ])
+    else:
+        cpudat = None
+    
+    return memdat, cpudat
+
+def get_node_assignments(num_ranks):
+    """Automatically assign node categories based on number of ranks"""
+    if num_ranks <= 8:
+        return ["background"] * num_ranks
+    else:
+        mid_point = num_ranks // 2
+        return ["background"] * mid_point + ["zoom"] * (num_ranks - mid_point)
+
+def main():
+    parser = argparse.ArgumentParser(description='Performance Analysis Visualization')
+    parser.add_argument('--use-balance-logs', action='store_true', 
+                       help='Force use of rank_*_balance.log files')
+    parser.add_argument('--use-dat-files', action='store_true',
+                       help='Force use of *_report-rank*-step*.dat files')
+    parser.add_argument('--use-thread-files', action='store_true',
+                       help='Force use of thread_info_MPI-step*.dat files')
+    args = parser.parse_args()
+    
+    # Color mapping
+    cmap = {"zoom": "tab:red", "background": "tab:blue", "compute": "tab:green"}
+    
+    # Try to load data
+    memdat, cpudat = None, None
+    thread_data, rank_thread_counts = None, None
+    
+    if args.use_thread_files:
+        thread_result = parse_thread_info_files()
+        if thread_result:
+            thread_data, rank_thread_counts = thread_result
+    elif args.use_dat_files:
+        memdat, cpudat = parse_dat_files()
+        thread_result = parse_thread_info_files()
+        if thread_result:
+            thread_data, rank_thread_counts = thread_result
+    elif args.use_balance_logs:
+        memdat, cpudat = parse_balance_logs()
+    else:
+        # Try balance logs first, then .dat files, then thread files
+        memdat, cpudat = parse_balance_logs()
+        if memdat is None or cpudat is None:
+            print("Balance logs not found, trying .dat files...")
+            memdat, cpudat = parse_dat_files()
+        
+        # Always try to get thread data
+        thread_result = parse_thread_info_files()
+        if thread_result:
+            thread_data, rank_thread_counts = thread_result
+    
+    if memdat is None and cpudat is None and thread_data is None:
+        print("Error: No suitable data files found!")
+        print("Looking for:")
+        print("  - rank_memory_balance.log and rank_cpu_balance.log")
+        print("  - memuse_report-rank*-step*.dat files")
+        print("  - mpiuse_report-rank*-step*.dat files")
+        print("  - thread_info_MPI-step*.dat files")
+        sys.exit(1)
+    
+    # Get unique ranks and steps
+    if memdat is not None:
+        unique_ranks = np.unique(memdat["rank"])
+        memory_steps = np.unique(memdat["step"])
+    else:
+        memory_steps = np.array([])
+        
+    if cpudat is not None:
+        if memdat is None:
+            unique_ranks = np.unique(cpudat["rank"])
+        cpu_steps = np.unique(cpudat["step"])
+    else:
+        cpu_steps = np.array([])
+        
+    if thread_data is not None:
+        if memdat is None and cpudat is None:
+            unique_ranks = np.array(sorted(thread_data.keys()))
+        all_thread_steps = set()
+        for rank_steps in thread_data.values():
+            all_thread_steps.update(rank_steps.keys())
+        thread_steps = np.array(sorted(all_thread_steps))
+    else:
+        thread_steps = np.array([])
+    
+    # Use appropriate step count for each page
+    balance_steps = np.union1d(memory_steps, cpu_steps)
+    
+    num_ranks = len(unique_ranks)
+    num_balance_steps = len(balance_steps)
+    num_thread_steps = len(thread_steps)
+    
+    print(f"Found {num_ranks} ranks and {num_balance_steps} balance steps")
+    print(f"Ranks: {unique_ranks}")
+    print(f"Balance Steps: {balance_steps}")
+    
+    # Set up node assignments
+    node_assignments = get_node_assignments(num_ranks)
+    
+    # Calculate accumulated values per rank
+    accumulated_memory = np.zeros(num_ranks)
+    accumulated_cpu = np.zeros(num_ranks)
+    accumulated_thread_time = np.zeros(num_ranks)
+    thread_efficiency = np.zeros(num_ranks)
+    max_memory = np.zeros(num_ranks)
+    
+    # Individual thread data for detailed visualization
+    individual_thread_data = {}  # rank -> {thread_id -> total_time}
+    
+    for i, rank in enumerate(unique_ranks):
+        # Memory analysis
+        if memdat is not None:
+            rank_mask_mem = memdat["rank"] == rank
+            rank_memory = memdat["resident"][rank_mask_mem]
+            if len(rank_memory) > 0:
+                accumulated_memory[i] = np.mean(rank_memory)  # Average memory usage
+                max_memory[i] = np.max(rank_memory)  # Peak memory usage
+        
+        # CPU analysis
+        if cpudat is not None:
+            rank_mask_cpu = cpudat["rank"] == rank
+            rank_cpu = cpudat["sum"][rank_mask_cpu]
+            if len(rank_cpu) > 0:
+                accumulated_cpu[i] = np.sum(rank_cpu)  # Total CPU time
+        
+        # Thread analysis
+        if thread_data is not None and rank in thread_data:
+            total_thread_time = 0
+            max_threads = rank_thread_counts.get(rank, 1)
+            thread_utilization = 0
+            
+            # Initialize individual thread data for this rank
+            individual_thread_data[rank] = {}
+            
+            for step, thread_times in thread_data[rank].items():
+                step_total = sum(thread_times.values())
+                total_thread_time += step_total
+                
+                # Accumulate individual thread times
+                for thread_id, thread_time in thread_times.items():
+                    if thread_id not in individual_thread_data[rank]:
+                        individual_thread_data[rank][thread_id] = 0
+                    individual_thread_data[rank][thread_id] += thread_time
+                
+                # Calculate thread utilization for this step
+                if thread_times:
+                    active_threads = len(thread_times)
+                    thread_utilization += active_threads / max_threads
+            
+            accumulated_thread_time[i] = total_thread_time / 1000000000.0  # Convert ticks to seconds
+            if len(thread_data[rank]) > 0:
+                thread_efficiency[i] = thread_utilization / len(thread_data[rank])  # Average thread efficiency
+    
+    # Set up plotting labels and colors
+    bar_labels = []
+    bar_colors = []
+    for i, node_assignment in enumerate(node_assignments):
+        if node_assignment in bar_labels:
+            bar_labels.append(f"_{node_assignment}")
+        else:
+            bar_labels.append(node_assignment)
+        bar_colors.append(cmap[node_assignment])
+    
+    # Create the two-page visualization
+    print("Creating performance analysis visualization...")
+    
+    with PdfPages("pa_vis.pdf") as pdffile:
+        
+        # PAGE 1: Memory Usage and CPU Usage
+        has_memory_data = memdat is not None and np.any(accumulated_memory > 0)
+        has_cpu_data = cpudat is not None and np.any(accumulated_cpu > 0)
+
+        if has_memory_data or has_cpu_data:
+            # Calculate number of plots needed
+            num_plots = int(has_memory_data) + int(has_cpu_data)
+            
+            # Fix: Increase figure width to accommodate both charts properly
+            fig1, axs1 = plt.subplots(1, 2, figsize=(16, 6))
+            
+            # Fix: Center the title properly by adjusting spacing and using figure-level suptitle
+            fig1.suptitle(f"Memory and CPU Performance Analysis\n{num_balance_steps} selected steps across {num_ranks} MPI ranks", 
+                         fontsize=14, ha='center', va='top', y=0.95)
+            
+            plot_idx = 0
+            
+            # Memory usage chart
+            if has_memory_data:
+                bars_mem = axs1[plot_idx].bar(unique_ranks, accumulated_memory, color=bar_colors, alpha=0.7)
+                axs1[plot_idx].axhline(np.mean(accumulated_memory), color="black", ls="dashed", lw=1.5, label="Average")
+                axs1[plot_idx].axhline(1024**2, color="red", ls="solid", lw=2, label="1 GB threshold") # 1024^2 KB
+                axs1[plot_idx].set_ylabel("Average Memory Usage [KB]")
+                axs1[plot_idx].set_xlabel("MPI Rank")
+                axs1[plot_idx].set_title("Memory Usage (Average over selected steps)")
+                axs1[plot_idx].legend(loc="lower right", fontsize=9)
+                axs1[plot_idx].grid(True, alpha=0.3)
+                
+                # Add text annotations for memory values
+                for i, (rank, mem_kb) in enumerate(zip(unique_ranks, accumulated_memory)):
+                    mem_gb = mem_kb / (1024**2)
+                    axs1[plot_idx].text(rank, mem_kb + 0.08*(1024**2), f"{mem_gb:.2f}GB", 
+                               ha='center', va='bottom', fontsize=10)
+
+                # Set Y-axis limits to provide more space
+                max_mem = np.max(accumulated_memory)
+                axs1[plot_idx].set_ylim(0, max_mem * 1.15)  # Add 15% padding at top
+                axs1[plot_idx].set_xticks(unique_ranks)
+                plot_idx += 1
+            
+            # CPU usage chart
+            if has_cpu_data:
+                bars_cpu = axs1[plot_idx].bar(unique_ranks, accumulated_cpu / 1000,  # Convert to seconds
+                                     color=bar_colors, alpha=0.7)
+                axs1[plot_idx].axhline(np.mean(accumulated_cpu) / 1000, color="black", 
+                               ls="dashed", lw=1.5, label="Average")
+                axs1[plot_idx].set_ylabel("Total CPU Time [seconds]")
+                axs1[plot_idx].set_xlabel("MPI Rank")
+                axs1[plot_idx].set_title("CPU Usage (Total over selected steps)")
+                
+                # Calculate and display efficiency
+                total_cpu_time = accumulated_cpu.sum()
+                max_cpu_time = accumulated_cpu.max()
+                efficiency = total_cpu_time / (num_ranks * max_cpu_time) if max_cpu_time > 0 else 0
+                
+                axs1[plot_idx].text(0.95, 0.95, 
+                           f"ε = {100*efficiency:.1f}%\nTotal: {total_cpu_time/1000:.1f}s",
+                           transform=axs1[plot_idx].transAxes, va="top", ha="right",
+                           bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgray", alpha=0.7))
+                
+                axs1[plot_idx].legend(loc="lower right", fontsize=9)
+                axs1[plot_idx].grid(True, alpha=0.3)
+                
+                # Add text annotations for CPU values
+                for i, (rank, cpu_s) in enumerate(zip(unique_ranks, accumulated_cpu / 1000)):
+                    axs1[plot_idx].text(rank, cpu_s + max(accumulated_cpu)/1000 * 0.05, f"{cpu_s:.1f}", 
+                               ha='center', va='bottom', fontsize=10)
+
+                # Set Y-axis limits to provide more space
+                max_cpu_s = np.max(accumulated_cpu) / 1000
+                axs1[plot_idx].set_ylim(0, max_cpu_s * 1.15)  # Add 15% padding at top
+                axs1[plot_idx].set_xticks(unique_ranks)
+                plot_idx += 1
+        
+        # Hide unused subplot if only one plot
+        if plot_idx == 1:
+            axs1[1].set_visible(False)
+        
+        # Fix: Adjust spacing for wider figure and better layout
+        plt.subplots_adjust(top=0.85, bottom=0.15, left=0.08, right=0.95, wspace=0.25)
+        plt.savefig(pdffile, format="pdf", bbox_inches='tight', dpi=150)
+        plt.close()
+        
+        # PAGE 2: Individual Thread Usage (separated by rank)
+        if thread_data is not None and individual_thread_data:
+            # Determine subplot layout based on number of ranks
+            num_ranks_with_threads = len(individual_thread_data)
+            
+            if num_ranks_with_threads == 1:
+                fig2, ax2 = plt.subplots(1, 1, figsize=(12, 6))
+                axs2 = [ax2]
+            elif num_ranks_with_threads == 2:
+                fig2, axs2 = plt.subplots(1, 2, figsize=(16, 6))
+            elif num_ranks_with_threads <= 4:
+                fig2, axs2 = plt.subplots(2, 2, figsize=(16, 10))
+                axs2 = axs2.flatten()
+            else:
+                # For more than 4 ranks, create a grid layout
+                ncols = min(3, num_ranks_with_threads)
+                nrows = (num_ranks_with_threads + ncols - 1) // ncols
+                fig2, axs2 = plt.subplots(nrows, ncols, figsize=(6*ncols, 5*nrows))
+                if nrows > 1:
+                    axs2 = axs2.flatten()
+                else:
+                    axs2 = [axs2] if ncols == 1 else axs2
+            
+            fig2.suptitle(f"Individual Thread Usage by Rank\n{num_thread_steps} selected steps across {num_ranks} MPI ranks", 
+                         fontsize=14)
+            
+            # Plot thread usage for each rank separately
+            rank_idx = 0
+            global_max_thread_time = 0
+            
+            # First pass: find global maximum for consistent y-axis scaling
+            for rank in sorted(individual_thread_data.keys()):
+                for thread_time in individual_thread_data[rank].values():
+                    thread_time_s = thread_time / 1000000000.0  # Convert to seconds
+                    global_max_thread_time = max(global_max_thread_time, thread_time_s)
+            
+            # Second pass: create plots
+            for rank in sorted(individual_thread_data.keys()):
+                if rank_idx >= len(axs2):
+                    break
+                    
+                ax = axs2[rank_idx]
+                
+                # Get thread data for this rank
+                rank_threads = individual_thread_data[rank]
+                thread_ids = sorted(rank_threads.keys())
+                thread_times = [rank_threads[tid] / 1000000000.0 for tid in thread_ids]  # Convert to seconds
+                thread_labels = [f"T{tid}" for tid in thread_ids]
+                
+                if thread_times:
+                    # Create bar chart for this rank's threads
+                    x_positions = range(len(thread_ids))
+                    rank_color = bar_colors[rank] if rank < len(bar_colors) else 'tab:gray'
+                    bars = ax.bar(x_positions, thread_times, color=rank_color, alpha=0.7)
+                    
+                    # Add average line for this rank
+                    rank_avg = np.mean(thread_times)
+                    ax.axhline(rank_avg, color="black", ls="dashed", lw=1.5, label=f"Rank {rank} Avg")
+                    
+                    # Set labels and title
+                    ax.set_ylabel("Thread Time [s]")
+                    ax.set_xlabel("Thread ID")
+                    ax.set_title(f"Rank {rank} Thread Usage")
+                    ax.legend(loc="lower right", fontsize=9)
+                    ax.grid(True, alpha=0.3)
+                    
+                    # Set x-axis labels
+                    ax.set_xticks(x_positions)
+                    ax.set_xticklabels(thread_labels)
+                    
+                    # Add text annotations for thread values
+                    for i, (x_pos, thread_time) in enumerate(zip(x_positions, thread_times)):
+                        if thread_time > 0:  # Only label non-zero values
+                            ax.text(x_pos, thread_time + global_max_thread_time * 0.02, f"{thread_time:.1f}", 
+                                   ha='center', va='bottom', fontsize=8, rotation=90)
+
+                    # Calculate thread balance metrics for this rank
+                    rank_total_time = sum(thread_times)
+                    rank_max_time = max(thread_times) if thread_times else 0
+                    rank_min_time = min(thread_times) if thread_times else 0
+                    rank_balance_ratio = rank_min_time / rank_max_time if rank_max_time > 0 else 0
+                    
+                    # Add efficiency information
+                    ax.text(0.98, 0.98, 
+                            f"Total: {rank_total_time:.1f} s\n"
+                            f"Balance: {rank_balance_ratio:.2f}\n"
+                            f"Threads: {len(thread_times)}",
+                            transform=ax.transAxes, va="top", ha="right",
+                            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgreen", alpha=0.7),
+                            fontsize=9)
+                    
+                    # Set consistent Y-axis limits across all subplots
+                    ax.set_ylim(0, global_max_thread_time * 1.15)
+                else:
+                    # No thread data for this rank
+                    ax.text(0.5, 0.5, f"No thread data\nfor Rank {rank}", 
+                           transform=ax.transAxes, ha='center', va='center',
+                           bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgray", alpha=0.7))
+                    ax.set_title(f"Rank {rank} Thread Usage")
+                    ax.set_xlabel("Thread ID")
+                    ax.set_ylabel("Thread Time [s]")
+                
+                rank_idx += 1
+            
+            # Hide unused subplots
+            for i in range(rank_idx, len(axs2)):
+                axs2[i].set_visible(False)
+            
+            plt.tight_layout()
+            plt.savefig(pdffile, format="pdf", bbox_inches='tight', dpi=150)
+            plt.close()
+    
+    # Print summary statistics
+    print("\n" + "="*60)
+    print("PERFORMANCE ANALYSIS SUMMARY")
+    print("="*60)
+    print(f"Simulation: {num_balance_steps} steps, {num_ranks} MPI ranks")
+
+    # Show full list for balance steps, first/last for thread steps
+    if num_balance_steps > 0:
+        balance_steps_list = ", ".join(map(str, balance_steps))
+        print(f"Memory & CPU steps analyzed: {balance_steps_list}")
+    else:
+        print("Memory & CPU steps analyzed: None")
+
+    if num_thread_steps > 0:
+        if num_thread_steps <= 10:
+            # Show all steps if not too many
+            thread_steps_list = ", ".join(map(str, thread_steps))
+            print(f"Thread steps analyzed: {thread_steps_list}")
+        else:
+            # Show first and last few steps
+            print(f"Thread steps analyzed: {thread_steps[0]} to {thread_steps[-1]} ({num_thread_steps} total steps)")
+            print(f"  First 5: {', '.join(map(str, thread_steps[:5]))}")
+            print(f"  Last 5: {', '.join(map(str, thread_steps[-5:]))}")
+    else:
+        print("Thread steps analyzed: None")
+
+    print()
+    
+    if memdat is not None and np.any(accumulated_memory > 0):
+        print("MEMORY USAGE:")
+        for i, rank in enumerate(unique_ranks):
+            print(f"  Rank {rank}: Avg = {accumulated_memory[i]/1024:.2f} GB, "
+                  f"Max = {max_memory[i]/1024:.2f} GB")
+        print(f"  Overall average: {np.mean(accumulated_memory)/1024:.2f} GB")
+        print(f"  Memory balance: {np.std(accumulated_memory)/np.mean(accumulated_memory):.1%}")
+    else:
+        print("MEMORY USAGE: No data available")
+    
+    print()
+    
+    if cpudat is not None and np.any(accumulated_cpu > 0):
+        print("CPU USAGE:")
+        total_cpu_time = accumulated_cpu.sum()
+        max_cpu_time = accumulated_cpu.max()
+        efficiency = total_cpu_time / (num_ranks * max_cpu_time) if max_cpu_time > 0 else 0
+        
+        for i, rank in enumerate(unique_ranks):
+            print(f"  Rank {rank}: Total = {accumulated_cpu[i]/1000:.1f} seconds")
+        print(f"  Overall total: {total_cpu_time/1000:.1f} seconds")
+        print(f"  Parallel efficiency: {100*efficiency:.1f}%")
+        if max_cpu_time > 0:
+            print(f"  Load balance ratio: {accumulated_cpu.min()/max_cpu_time:.2f}")
+    else:
+        print("CPU USAGE: No data available")
+    
+    print()
+    
+    if individual_thread_data:
+        print("INDIVIDUAL THREAD USAGE:")
+        total_threads = sum(len(threads) for threads in individual_thread_data.values())
+        all_times = []
+        for rank, threads in individual_thread_data.items():
+            for thread_id, thread_time in threads.items():
+                thread_time_ms = thread_time / 1000000000.0
+                all_times.append(thread_time_ms)
+                print(f"  Rank {rank} Thread {thread_id}: {thread_time_ms:.1f} s")
+        
+        if all_times:
+            total_thread_time = sum(all_times)
+            max_thread_time = max(all_times)
+            min_thread_time = min(all_times)
+            balance_ratio = min_thread_time / max_thread_time if max_thread_time > 0 else 0
+            
+            print(f"  Total threads active: {total_threads}")
+            print(f"  Overall total thread time: {total_thread_time:.1f} s")
+            print(f"  Thread balance ratio: {balance_ratio:.2f}")
+    else:
+        print("INDIVIDUAL THREAD USAGE: No data available")
+    
+    print()
+    print(f"Output saved to: pa_vis.pdf")
+    print("="*60)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

From b06d90f5b16001b63310dd18e32792616453c65e Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Wed, 11 Jun 2025 08:59:27 +0100
Subject: [PATCH 36/43] Correct location of the pav script from previous commit

---
 examples/HumanMobility/{data-rivers-1-2-32 => }/pa_vis.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/HumanMobility/{data-rivers-1-2-32 => }/pa_vis.py (100%)

diff --git a/examples/HumanMobility/data-rivers-1-2-32/pa_vis.py b/examples/HumanMobility/pa_vis.py
similarity index 100%
rename from examples/HumanMobility/data-rivers-1-2-32/pa_vis.py
rename to examples/HumanMobility/pa_vis.py

From 9bbe5fc02584fa2d3718611f4ea558475c001d0c Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Thu, 12 Jun 2025 12:00:40 +0100
Subject: [PATCH 37/43] Rework and create 2 separate installation scripts for
 old Cosma (intel2024) and new Cosma5 (intel2025) due to missing AVX support
 on old Cosma

---
 install_swift_cosma.sh     |  77 ++++++++++++++++-----
 install_swift_intel2025.sh | 135 +++++++++++++++++++++++++++++++++++++
 2 files changed, 196 insertions(+), 16 deletions(-)
 create mode 100755 install_swift_intel2025.sh

diff --git a/install_swift_cosma.sh b/install_swift_cosma.sh
index 4b66100ea..1dec6118a 100755
--- a/install_swift_cosma.sh
+++ b/install_swift_cosma.sh
@@ -5,9 +5,9 @@
 #SBATCH --cpus-per-task=16        # Use all cores for parallel make
 #SBATCH --mem=64G                 # Memory for compilation
 #SBATCH -J sw-build              # Job name
-##SBATCH -o sw.out               # Output file
-##SBATCH -e sw.err               # Error file
-#SBATCH -p cosma8-shm               # COSMA5 partition
+#SBATCH -o sw_cosma.out               # Output file
+#SBATCH -e sw_cosma.err               # Error file
+#SBATCH -p cosma               # COSMA5 partition
 #SBATCH -A durham               # Account
 #SBATCH -t 0:30:00             # Increased time for compilation
 
@@ -19,7 +19,8 @@ source ~/swift-env/bin/activate
 #PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
 
 module purge
-module load intel_comp/2025.0.1 #intel_comp/2024.2.0
+module load cosma
+module load intel_comp/2024.2.0 #intel_comp/2025.0.1
 module load umf compiler-rt tbb compiler mpi
 # module load openmpi/5.0.3
 module load python
@@ -29,13 +30,13 @@ module load parmetis/4.0.3-64bit
 module load parallel_hdf5/1.14.4
 module load sundials/5.8.0_c8_single
 module load ffmpeg
-module load likwid/5.4.1
+# module load likwid/5.4.1
 
 # Set number of threads for make
 export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
-export AR=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ar
-export LD=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-link
-export RANLIB=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ranlib
+# export AR=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ar
+# export LD=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-link
+# export RANLIB=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ranlib
 
 module list
 
@@ -49,13 +50,61 @@ make clean
 echo "#############################"
 echo "# Running './configure' ... #"
 echo "#############################"
-# /configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
+#===============================================================================
+# SWIFT Configuration for Human Mobility Analysis
+#===============================================================================
+#
+# This configuration builds SWIFT with the following features:
+#
+# DEBUGGING & PROFILING:
+#   --enable-debug           : Enable debug symbols and consistency checks
+#   --enable-task-debugging  : Enable detailed task-level performance analysis
+#                             (generates thread_info_MPI-step*.dat files)
+#
+# PARALLELIZATION:
+#   --enable-mpi            : MPI parallel support for multi-node execution
+#   --enable-parallel-hdf5  : Parallel HDF5 I/O for efficient data output
+#   --with-tbbmalloc        : Intel TBB memory allocator for performance
+#   --with-parmetis         : ParMETIS library for domain decomposition
+#
+# HUMAN MOBILITY MODEL:
+#   --with-hydro=abm              : Agent-based modeling scheme (a placeholder for future ABM schemes, separating it from the hydro scheme)
+#   --with-hydro-dimension=2      : 2D simulation space
+#   --with-abm=human-mobility     : Human mobility implementation (a placeholder for future ABM implementations)
+#   --with-ext-potential=human-mobility : External potential for human mobility
+#
+# HUMAN MOBILITY SCENARIOS (--with-hm options):
+#   --with-hm=none          : No human mobility scenario (default)
+#                             Disables all HM-specific features
+#
+#   --with-hm=all           : Enable all human mobility scenarios
+#                             Includes both river and random-walk cases
+#                             Activates HM_CASE_RIVER and HM_CASE_RANDOMWALK
+#
+#   --with-hm=river         : River-based human mobility scenario
+#                             Enables river geography
+#                             Activates HM_CASE_RIVER preprocessor definition
+#
+#   --with-hm=random-walk   : Random walk human mobility scenario  
+#                             Initiate human random walk which consequently is influenced by other humans and geographic features
+#                             Activates HM_CASE_RANDOMWALK preprocessor definition
+#
+# NOTE: Human mobility scenarios require:
+#       - --with-abm=human-mobility (mandatory)
+#       - --with-hydro=abm (mandatory)
+#       - --with-ext-potential=human-mobility (recommended)
+#       - --with-hydro-dimension=2 (typical for HM studies)
+#
+#===============================================================================
+#/configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
 #export CFLAGS="-fsanitize=address -g"
+# --program-suffix=_intel2025 \
 ./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
-    --program-suffix=_intel2025 \
-    CFLAGS="-Wno-error=gnu-folding-constant -g" \
+    --program-suffix=_cosma \
+    CFLAGS="-Wno-error=gnu-folding-constant" \
     LDFLAGS= \
-    --enable-ipo \
+    --enable-debug \
+    --enable-task-debugging \
     --enable-mpi \
     --enable-parallel-hdf5 \
     --with-tbbmalloc \
@@ -66,10 +115,6 @@ echo "#############################"
     --with-ext-potential=human-mobility \
     --with-hm=all
 
-		# --enable-debug \
-	    # --with-hm=random-walk
-
-
 echo "########################################"
 echo "# Running 'make -j\$(nproc --all)' ... #"
 echo "########################################"
diff --git a/install_swift_intel2025.sh b/install_swift_intel2025.sh
new file mode 100755
index 000000000..c61e27cd5
--- /dev/null
+++ b/install_swift_intel2025.sh
@@ -0,0 +1,135 @@
+#!/bin/bash
+
+#SBATCH --nodes=1                  # Request 1 node
+#SBATCH --ntasks=1                # Single task for compilation
+#SBATCH --cpus-per-task=32        # Use all cores for parallel make
+#SBATCH --mem=64G                 # Memory for compilation
+#SBATCH -J sw-build              # Job name
+#SBATCH -o sw_intel2025.out               # Output file
+#SBATCH -e sw_intel2025.err               # Error file
+#SBATCH -p cosma5               # COSMA5 partition
+#SBATCH -A durham               # Account
+#SBATCH -t 0:30:00             # Increased time for compilation
+
+source ~/swift-env/bin/activate
+
+# Adapt the following lines to your system
+#LOCAL_LIBRARY_PATH=/home/dmitry/local/lib
+#METIS_PATH=/usr/lib/x86_64-linux-gnu
+#PARMETIS_PATH=/usr/lib/x86_64-linux-gnu
+
+module purge
+module load cosma
+module load intel_comp/2025.0.1 #intel_comp/2024.2.0
+module load umf compiler-rt tbb compiler mpi
+# module load openmpi/5.0.3
+module load python
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+module load ffmpeg
+# module load likwid/5.4.1
+
+# Set number of threads for make
+export MAKEFLAGS="-j$SLURM_CPUS_PER_TASK"
+export AR=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ar
+export LD=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-link
+export RANLIB=/cosma/local/intel/oneAPI_2025.0.1/compiler/2025.0/bin/compiler/llvm-ranlib
+
+module list
+
+# The main installation script for SWIFT_ABM
+
+echo "############################"
+echo "# Running 'make clean' ... #"
+echo "############################"
+make clean
+
+echo "#############################"
+echo "# Running './configure' ... #"
+echo "#############################"
+#===============================================================================
+# SWIFT Configuration for Human Mobility Analysis
+#===============================================================================
+#
+# This configuration builds SWIFT with the following features:
+#
+# DEBUGGING & PROFILING:
+#   --enable-debug           : Enable debug symbols and consistency checks
+#   --enable-task-debugging  : Enable detailed task-level performance analysis
+#                             (generates thread_info_MPI-step*.dat files)
+#
+# PARALLELIZATION:
+#   --enable-mpi            : MPI parallel support for multi-node execution
+#   --enable-parallel-hdf5  : Parallel HDF5 I/O for efficient data output
+#   --with-tbbmalloc        : Intel TBB memory allocator for performance
+#   --with-parmetis         : ParMETIS library for domain decomposition
+#
+# HUMAN MOBILITY MODEL:
+#   --with-hydro=abm              : Agent-based modeling scheme (a placeholder for future ABM schemes, separating it from the hydro scheme)
+#   --with-hydro-dimension=2      : 2D simulation space
+#   --with-abm=human-mobility     : Human mobility implementation (a placeholder for future ABM implementations)
+#   --with-ext-potential=human-mobility : External potential for human mobility
+#
+# HUMAN MOBILITY SCENARIOS (--with-hm options):
+#   --with-hm=none          : No human mobility scenario (default)
+#                             Disables all HM-specific features
+#
+#   --with-hm=all           : Enable all human mobility scenarios
+#                             Includes both river and random-walk cases
+#                             Activates HM_CASE_RIVER and HM_CASE_RANDOMWALK
+#
+#   --with-hm=river         : River-based human mobility scenario
+#                             Enables river geography
+#                             Activates HM_CASE_RIVER preprocessor definition
+#
+#   --with-hm=random-walk   : Random walk human mobility scenario  
+#                             Initiate human random walk which consequently is influenced by other humans and geographic features
+#                             Activates HM_CASE_RANDOMWALK preprocessor definition
+#
+# NOTE: Human mobility scenarios require:
+#       - --with-abm=human-mobility (mandatory)
+#       - --with-hydro=abm (mandatory)
+#       - --with-ext-potential=human-mobility (recommended)
+#       - --with-hydro-dimension=2 (typical for HM studies)
+#
+#===============================================================================
+#/configure --prefix=/cosma/home/do009/dc-niko3/.local/ \
+#export CFLAGS="-fsanitize=address -g"
+./configure --prefix=/cosma5/data/durham/dc-niko3/.local/ \
+    --program-suffix=_intel2025 \
+    CFLAGS="-Wno-error=gnu-folding-constant" \
+    LDFLAGS= \
+    --enable-debug \
+    --enable-task-debugging \
+    --enable-mpi \
+    --enable-parallel-hdf5 \
+    --with-tbbmalloc \
+    --with-parmetis \
+    --with-hydro=abm \
+    --with-hydro-dimension=2 \
+    --with-abm=human-mobility \
+    --with-ext-potential=human-mobility \
+    --with-hm=all
+
+echo "########################################"
+echo "# Running 'make -j\$(nproc --all)' ... #"
+echo "########################################"
+make
+
+echo "#####################################"
+echo "# Running 'ranlib' on libraries ... #"
+echo "#####################################"
+ranlib src/.libs/libswiftsim.a argparse/.libs/libargparse.a src/.libs/libswiftsim_mpi.a
+
+echo "############################"
+echo "# Running 'make' again ... #"
+echo "############################"
+make
+
+echo "###################################"
+echo "# Running 'sudo make install' ... #"
+echo "###################################"
+make install

From b6ed8b122519526b3a52f2435d0e5a604ca55c73 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Thu, 12 Jun 2025 12:26:30 +0100
Subject: [PATCH 38/43] Rework job scripts and add as SLURM job scheduling
 suite for perf analysis of human mobility scenario examples

---
 examples/HumanMobility/humanMobility.yml      |  75 ------------
 .../HumanMobility/humanMobility_template.yml  |  16 ++-
 examples/HumanMobility/job.sh                 |  86 ++++++++++----
 examples/HumanMobility/job_cosma.sh           | 103 ++++++++++++++++
 examples/HumanMobility/perf/job_scheduling.md | 104 ++++-------------
 examples/HumanMobility/run.sh                 |  82 +++++++------
 examples/HumanMobility/run_advisor.sh         |  71 +++++++++++
 examples/HumanMobility/run_aps.sh             |  71 +++++++++++
 examples/HumanMobility/run_cosma.sh           |  54 +++++++++
 examples/HumanMobility/run_inspector.sh       |  63 ++++++++++
 examples/HumanMobility/run_likwid.sh          | 110 ++++++++++++------
 examples/HumanMobility/run_maqao.sh           |  47 +++++---
 examples/HumanMobility/run_scorep.sh          |  67 +++++++++++
 examples/HumanMobility/run_vtune.sh           |  63 ++++++++++
 examples/HumanMobility/submit.sh              |  89 +++++++++++++-
 15 files changed, 826 insertions(+), 275 deletions(-)
 delete mode 100644 examples/HumanMobility/humanMobility.yml
 create mode 100644 examples/HumanMobility/job_cosma.sh
 create mode 100755 examples/HumanMobility/run_advisor.sh
 create mode 100755 examples/HumanMobility/run_aps.sh
 create mode 100755 examples/HumanMobility/run_cosma.sh
 create mode 100755 examples/HumanMobility/run_inspector.sh
 create mode 100755 examples/HumanMobility/run_scorep.sh
 create mode 100755 examples/HumanMobility/run_vtune.sh

diff --git a/examples/HumanMobility/humanMobility.yml b/examples/HumanMobility/humanMobility.yml
deleted file mode 100644
index 2e1fbff26..000000000
--- a/examples/HumanMobility/humanMobility.yml
+++ /dev/null
@@ -1,75 +0,0 @@
-# Define the system of units to use internally. 
-InternalUnitSystem:
-  UnitMass_in_cgs:     1 #e+3          # every human has mass 1
-  UnitLength_in_cgs:   1 #e+3        # 1 meter
-  UnitVelocity_in_cgs: 1 #100        # 1 meter per second
-  UnitCurrent_in_cgs:  1          # Amperes
-  UnitTemp_in_cgs:     1          # Kelvin
-
-# Parameters governing the time integration
-TimeIntegration:
-  time_begin: 0.    # The starting time of the simulation (in internal units).
-  time_end:   10 #43200 # minutes in a month  # The end time of the simulation (in internal units).
-  dt_min:     1e-7     # minute #1e-5  # The minimal time-step size of the simulation (in internal units).
-  dt_max:     1e-1 #1440   # The maximal time-step size of the simulation (in internal units).
-
-# Parameters governing the snapshots
-Snapshots:
-  subdir:              data-maqao
-  basename:            humanMobility # Common part of the name of output files
-  time_first:          0.         # Time of the first output (in internal units)
-  delta_time:          1e-2 #1440        # Time difference between consecutive outputs (in internal units)
-
-# Parameters governing the conserved quantities statistics
-Statistics:
-  delta_time:          1e-2 #60 # Time between statistics output
-
-# Parameters for the hydrodynamics scheme
-SPH:
-  resolution_eta:        1.2348   # Target smoothing length in units of the mean inter-particle separation (1.2348 == 48Ngbs with the cubic spline kernel).
-  CFL_condition:         0.1      # Courant-Friedrich-Levy condition for time integration.
-
-Scheduler:
-  max_top_level_cells: 16
-  tasks_per_cell: 64
-  # links_per_tasks: 1024
-
-# Parameters related to the initial conditions
-InitialConditions:
-  file_name:  humans-maqao.hdf5     # The file to read
-  periodic:   1
-
-PhysicalConstants:
-  G: 1.0  # Gravitational constant in internal units
-
-# Add these gravity parameters
-Gravity:
-  MAC: adaptive           # Multipole Acceptance Criterion (adaptive/geometric)
-  mesh_side_length: 32     # Number of cells in each dimension of the gravity mesh
-  scale_factor_a: 1.0      # Scale factor for cosmological simulations (1.0 for non-cosmological)
-  theta: 0.7              # Opening angle for the gravity calculation
-  theta_cr: 0.5          # Critical opening angle for adaptive MAC
-  theta_min: 0.0         # Minimum opening angle
-  max_smoothing_iterations: 3  # Maximum number of iterations for force smoothing
-  epsilon_fmm: 0.001     # Accuracy parameter for FMM
-  eta: 0.025             # Constant controlling force accuracy in tree-PM algorithm
-  comoving_DM_softening: 0.0  # Softening length for gravity in internal units
-  max_physical_DM_softening: 0.0  # Max physical softening length
-  comoving_baryon_softening: 1.0  # Softening length for gas particles
-  max_physical_baryon_softening: 1.0  # Max physical softening for gas
-  mesh_uses_cell_grad: 1   # Whether to use cell gradients in mesh gravity
-  periodic: 1             # Use periodic gravity (matches InitialConditions)
-
-# ConstantPotential:
-#   g_cgs:      [0., -98, 0.]  # Earth acceleration along z-axis (cgs units)
-
-# PointMassPotential:
-#   position:         [4950., 4950., 0.]  # Location of the point mass (internal units)
-#   useabspos:                   1  # Use absolute positions (0 for relative to centre)
-#   mass:                      1e14  # Mass of the point (internal units)
-#   # timestep_mult:             0.1  # (Optional) The dimensionless constant C in the time-step condition
-
-RiverPotential:
-#  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
-#  mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  river-maqao.hdf5 # Path to acceleration field HDF5 file
diff --git a/examples/HumanMobility/humanMobility_template.yml b/examples/HumanMobility/humanMobility_template.yml
index 98d1f4851..24156c0a5 100644
--- a/examples/HumanMobility/humanMobility_template.yml
+++ b/examples/HumanMobility/humanMobility_template.yml
@@ -15,7 +15,7 @@ TimeIntegration:
 
 # Parameters governing the snapshots
 Snapshots:
-  subdir:              ${DATA}
+  subdir:              .          # Current directory (since we cd into ${DATA})
   basename:            ${HUMANMOBILITY} # Common part of the name of output files
   time_first:          0.         # Time of the first output (in internal units)
   delta_time:          1e-2 #1440        # Time difference between consecutive outputs (in internal units)
@@ -36,7 +36,7 @@ Scheduler:
 
 # Parameters related to the initial conditions
 InitialConditions:
-  file_name:  ${HUMANS}.hdf5     # The file to read
+  file_name:  ../${HUMANS}.hdf5     # The file to read (relative to data directory)
   periodic:   1
 
 PhysicalConstants:
@@ -70,6 +70,12 @@ Gravity:
 #   # timestep_mult:             0.1  # (Optional) The dimensionless constant C in the time-step condition
 
 RiverPotential:
-#  position:        [5010., 5090.]  # y-coordinates of the river banks (internal units)
-#  mass:     1e6  # "Mass" of the "river" (internal units)
-  parameter_file:  ${RIVERS}.hdf5 # Path to acceleration field HDF5 file
+  parameter_file:  ../${RIVERS}.hdf5 # Path to acceleration field HDF5 file (relative to data directory)
+
+DomainDecomposition:
+  trigger:              0.05     # Enable load balancing analysis
+  initial_type:         memory
+  repartition_type:     timecosts
+  use_fixed_costs:      0
+  memory_reports:       1        # Enable memory balance logging
+  cpu_reports:          1        # Enable CPU balance logging
diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index cdeef0a57..1d8452464 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -1,67 +1,103 @@
 #!/bin/bash
-
-#SBATCH --ntasks=4              # Total number of MPI tasks (cores) (max 16)
-#SBATCH --nodes=1               # Number of nodes
-#SBATCH --ntasks-per-node=4     # MPI tasks per node (max 16)
-#SBATCH --cpus-per-task=4      # CPU cores per MPI rank
-#SBATCH --mem=64G               # Memory per node
-#SBATCH -p cosma8-shm           # COSMA5 partition
+#SBATCH -p cosma5               # COSMA5 partition (with AVX support)
 #SBATCH -A durham               # Account
 #SBATCH -t 12:00:00             # Time limit hrs:min:sec
 #SBATCH --mail-type=END
-#SBATCH --mail-user=lcgk69@durham.ac.uk
+##SBATCH --mail-user=<INSERT YOUR EMAIL ADDRESS>
+# Note: Some SLURM parameters are now passed from submit.sh, not hardcoded here
 
 # Check if an argument was provided
 if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--gen|--run|--vis|--map]"
+    echo "Usage: $0 [--gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep]"
     exit 1
 fi
 
 mode=$1
 
 case $mode in
-    --gen|--run|--vis|--map)
+    --gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
         ;;
     *)
-        echo "Invalid argument. Use --gen, --run, --vis or --map"
+        echo "Invalid argument. Use --gen, --vis, --map, --run, --likwid, --maqao, --aps, --vtune, --advisor, --inspector, or --scorep"
         exit 1
         ;;
 esac
 
-# Common module loading for both modes
+# Display current job configuration
+echo "Job Configuration:"
+echo "  Mode: $mode"
+echo "  Nodes: ${SLURM_JOB_NUM_NODES:-1}"
+echo "  MPI Tasks: ${SLURM_NTASKS:-1}"
+echo "  CPUs per Task: ${SLURM_CPUS_PER_TASK:-16}"
+echo "  Memory per Node: ${SLURM_MEM_PER_NODE:-64G}"
+echo "  Partition: COSMA5 (modern hardware)"
+
+# Common module loading
 module purge
 module load cosma
 module load intel_comp/2025.0.1 # gnu_comp/14.1.0 intel_comp/2024.2.0
 module load umf compiler-rt tbb compiler mpi
 # module load openmpi/5.0.3/
+
+# Set MPI environment variables
+export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
+export I_MPI_FABRICS=shm:ofi
+
 module load python
 module load fftw/3.3.10
 module load gsl
 module load parmetis/4.0.3-64bit
 module load parallel_hdf5/1.14.4
 module load sundials/5.8.0_c8_single
-module load maqao #likwid/5.4.1
-
 
-# Additional module for visualisation
-if [ "$mode" = "--vis" ]; then
-    module load ffmpeg
-fi
-
-module list
-
-# Execute the appropriate script
+# Load analysis-specific modules and execute the appropriate script
 case $mode in
     --gen)
         ./gen.sh
         ;;
-    --run)
-        ./run_maqao.sh
-        ;;
     --vis)
+        module load ffmpeg
         ./visualise.sh
         ;;
     --map)
         ./map.sh
         ;;
+    --run)
+        ./run.sh
+        ;;
+    --likwid)
+		# Load likwid profiler
+        module load likwid/5.4.1
+        ./run_likwid.sh
+        ;;
+    --maqao)
+		# Load Maqao profiler
+        module load maqao
+        ./run_maqao.sh
+        ;;
+    --aps)
+        # Load APS profiler
+        source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
+        ./run_aps.sh
+        ;;
+    --vtune)
+        # Load VTune profiler
+        source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
+        ./run_vtune.sh
+        ;;
+    --advisor)
+        # Load Intel Advisor 2025.0
+        source /cosma/local/intel/oneAPI_2025.0.1/advisor/2025.0/advisor-vars.sh
+        ./run_advisor.sh
+        ;;
+    --inspector)
+        # Load Intel Inspector
+        source /cosma/local/intel/oneAPI_2023.2.0/inspector/2023.2.0/inspxe-vars.sh
+        ./run_inspector.sh
+        ;;
+    --scorep)
+        # Load Score-P for detailed instrumentation
+        module load scorep/8.4
+        ./run_scorep.sh
+        ;;
 esac
diff --git a/examples/HumanMobility/job_cosma.sh b/examples/HumanMobility/job_cosma.sh
new file mode 100644
index 000000000..b230a1bc3
--- /dev/null
+++ b/examples/HumanMobility/job_cosma.sh
@@ -0,0 +1,103 @@
+#!/bin/bash
+#SBATCH -p cosma                # COSMA partition (legacy)
+#SBATCH -A durham               # Account
+#SBATCH -t 12:00:00             # Time limit hrs:min:sec
+#SBATCH --mail-type=END
+##SBATCH --mail-user=<INSERT YOUR EMAIL ADDRESS>
+# Note: Some SLURM parameters are now passed from submit.sh, not hardcoded here
+
+# Check if an argument was provided
+if [ $# -ne 1 ]; then
+    echo "Usage: $0 [--gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep]"
+    exit 1
+fi
+
+mode=$1
+
+case $mode in
+    --gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
+        ;;
+    *)
+        echo "Invalid argument. Use --gen, --vis, --map, --run, --likwid, --maqao, --aps, --vtune, --advisor, --inspector, or --scorep"
+        exit 1
+        ;;
+esac
+
+# Display current job configuration
+echo "Job Configuration:"
+echo "  Mode: $mode"
+echo "  Partition: COSMA (legacy hardware)"
+echo "  Nodes: ${SLURM_JOB_NUM_NODES:-1}"
+echo "  MPI Tasks: ${SLURM_NTASKS:-1}"
+echo "  CPUs per Task: ${SLURM_CPUS_PER_TASK:-16}"
+echo "  Memory per Node: ${SLURM_MEM_PER_NODE:-64G}"
+
+# Common module loading - use older, compatible modules for COSMA
+module purge
+module load cosma
+module load intel_comp/2024.2.0    # Use older Intel compiler for compatibility
+module load compiler-rt tbb compiler mpi
+# module load openmpi/5.0.3/
+
+# Set MPI environment variables
+export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
+export I_MPI_FABRICS=shm:ofi
+
+module load python
+module load fftw/3.3.10
+module load gsl
+module load parmetis/4.0.3-64bit
+module load parallel_hdf5/1.14.4
+module load sundials/5.8.0_c8_single
+
+# Load analysis-specific modules and execute the appropriate script
+case $mode in
+    --gen)
+        ./gen.sh
+        ;;
+    --vis)
+        module load ffmpeg
+        ./visualise.sh
+        ;;
+    --map)
+        ./map.sh
+        ;;
+    --run)
+        ./run_cosma.sh  # Use COSMA-compatible run script
+        ;;
+    --likwid)
+		# Load likwid profiler
+        module load likwid/5.4.1
+        ./run_likwid.sh
+        ;;
+    --maqao)
+		# Load Maqao profiler
+        module load maqao
+        ./run_maqao.sh
+        ;;
+    --aps)
+        echo "Warning: Intel APS 2025 may not be compatible with legacy COSMA hardware"
+        source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
+        ./run_aps.sh
+        ;;
+    --vtune)
+        echo "Warning: Intel VTune 2025 may not be compatible with legacy COSMA hardware"
+        source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
+        ./run_vtune.sh
+        ;;
+    --advisor)
+        echo "Warning: Intel Advisor 2025 may not be compatible with legacy COSMA hardware"
+        source /cosma/local/intel/oneAPI_2025.0.1/advisor/2025.0/advisor-vars.sh
+        ./run_advisor.sh
+        ;;
+    --inspector)
+        # Load Intel Inspector
+        source /cosma/local/intel/oneAPI_2023.2.0/inspector/2023.2.0/inspxe-vars.sh
+        ./run_inspector.sh
+        ;;
+    --scorep)
+        # Load Score-P for detailed instrumentation
+        module load scorep/8.4
+        ./run_scorep.sh
+        ;;
+esac
diff --git a/examples/HumanMobility/perf/job_scheduling.md b/examples/HumanMobility/perf/job_scheduling.md
index 3fbabeef9..8350faf23 100644
--- a/examples/HumanMobility/perf/job_scheduling.md
+++ b/examples/HumanMobility/perf/job_scheduling.md
@@ -57,125 +57,71 @@ To conduct performance analysis, we have the following queues:
 
 #### Strong scaling
 
-**Intel Advisor Analysis for Strong Scaling**
+**Performance Analysis for Strong Scaling**
 
-For strong scaling analysis with Intel Advisor, we keep the problem size constant (100 x 100 humans on 10 x 10 km) and vary the number of computational resources:
+For strong scaling analysis, we keep the problem size constant (100 x 100 humans on 10 x 10 km) and vary the number of computational resources:
 
 **Base Configuration (Serial Baseline):**
 ```bash
-./submit.sh --advisor -N 1 -n 1 -c 1     # 1 node, 1 MPI rank, 1 thread (serial)
+./submit.sh --run -N 1 -n 1 -c 1     # 1 node, 1 MPI rank, 1 thread (serial)
 ```
 
 **Intra-node Scaling (COSMA5 queue - threading analysis):**
 ```bash
 # Test threading efficiency within a single node
-./submit.sh --advisor -N 1 -n 1 -c 16    # 1 node, 1 MPI rank, 16 threads
-./submit.sh --advisor -N 1 -n 1 -c 32    # 1 node, 1 MPI rank, 32 threads  
-./submit.sh --advisor -N 1 -n 1 -c 64    # 1 node, 1 MPI rank, 64 threads
-./submit.sh --advisor -N 1 -n 1 -c 128   # 1 node, 1 MPI rank, 128 threads
-./submit.sh --advisor -N 1 -n 1 -c 256   # 1 node, 1 MPI rank, 256 threads (max COSMA5)
+./submit.sh --run -N 1 -n 1 -c 16    # 1 node, 1 MPI rank, 16 threads
+./submit.sh --run -N 1 -n 1 -c 32    # 1 node, 1 MPI rank, 32 threads  
+./submit.sh --run -N 1 -n 1 -c 64    # 1 node, 1 MPI rank, 64 threads
+./submit.sh --run -N 1 -n 1 -c 128   # 1 node, 1 MPI rank, 128 threads
+./submit.sh --run -N 1 -n 1 -c 256   # 1 node, 1 MPI rank, 256 threads (max COSMA5)
 ```
 
 **Inter-node Scaling (COSMA5 queue - MPI analysis):**
 ```bash
 # Test MPI scaling across multiple nodes with fixed threads per rank
-./submit.sh --advisor -N 2 -n 2 -c 128   # 2 nodes, 2 MPI ranks, 128 threads each
-./submit.sh --advisor -N 3 -n 3 -c 85    # 3 nodes, 3 MPI ranks, ~85 threads each
+./submit.sh --run -N 2 -n 2 -c 128   # 2 nodes, 2 MPI ranks, 128 threads each
+./submit.sh --run -N 3 -n 3 -c 85    # 3 nodes, 3 MPI ranks, ~85 threads each
 ```
 
 **COSMA queue comparison:**
 ```bash
 # Compare with older COSMA hardware
-./submit.sh --advisor -N 1 -n 1 -c 16    # 1 old COSMA node, 1 MPI rank, 16 threads (max)
-./submit.sh --advisor -N 2 -n 2 -c 8     # 2 old COSMA nodes, 2 MPI ranks, 8 threads each
-./submit.sh --advisor -N 4 -n 4 -c 4     # 4 old COSMA nodes, 4 MPI ranks, 4 threads each
+./submit.sh --run -N 1 -n 1 -c 16 -p cosma   # 1 old COSMA node, 1 MPI rank, 16 threads (max)
+./submit.sh --run -N 2 -n 2 -c 8 -p cosma    # 2 old COSMA nodes, 2 MPI ranks, 8 threads each
+./submit.sh --run -N 4 -n 4 -c 4 -p cosma    # 4 old COSMA nodes, 4 MPI ranks, 4 threads each
 ```
 
-**Intel Advisor Analysis Focus:**
-- **Survey Analysis**: Identify hotspots and vectorization opportunities
-- **Tripcounts Analysis**: Analyze loop characteristics and iteration counts
-- **Map Analysis**: Detailed vectorization recommendations
-- **Dependencies Analysis**: Identify loop-carried dependencies preventing vectorization
-- **Memory Access Patterns**: Understand cache efficiency and memory bottlenecks
-
 #### Weak scaling
 
-**Intel Advisor Analysis for Weak Scaling**
+**Performance Analysis for Weak Scaling**
 
 For weak scaling, we maintain constant work per computational unit by increasing problem size proportionally with resources:
 
 **Baseline (2 MPI ranks, 100 x 100 humans):**
 ```bash
-./submit.sh --advisor -N 1 -n 2 -c 8     # Base: 100x100 humans, 2 ranks, 8 threads each
+./submit.sh --run -N 1 -n 2 -c 8     # Base: 100x100 humans, 2 ranks, 8 threads each
 ```
 
 **Scale to 8 MPI ranks (200 x 200 humans):**
 ```bash
 # COSMA5 queue (high core count nodes)
-./submit.sh --advisor -N 1 -n 8 -c 32    # 1 node, 8 ranks, 32 threads each (256 total cores)
+./submit.sh --run -N 1 -n 8 -c 32    # 1 node, 8 ranks, 32 threads each (256 total cores)
 
 # COSMA queue (distributed across multiple nodes)
-./submit.sh --advisor -N 2 -n 8 -c 2     # 2 nodes, 8 ranks, 2 threads each
-./submit.sh --advisor -N 4 -n 8 -c 2     # 4 nodes, 8 ranks, 2 threads each  
-./submit.sh --advisor -N 8 -n 8 -c 2     # 8 nodes, 8 ranks, 2 threads each
+./submit.sh --run -N 2 -n 8 -c 2 -p cosma    # 2 nodes, 8 ranks, 2 threads each
+./submit.sh --run -N 4 -n 8 -c 2 -p cosma    # 4 nodes, 8 ranks, 2 threads each  
+./submit.sh --run -N 8 -n 8 -c 2 -p cosma    # 8 nodes, 8 ranks, 2 threads each
 ```
 
-**Scale to 18 MPI ranks (300 x 300 humans):**
+**Scale to 16 MPI ranks (300 x 300 humans):**
 ```bash
 # COSMA5 queue (maximum utilization)
-./submit.sh --advisor -N 1 -n 18 -c 14   # 1 node, 18 ranks, ~14 threads each
+./submit.sh --run -N 1 -n 18 -c 14   # 1 node, 18 ranks, ~14 threads each
 
 # COSMA queue (distributed)
-./submit.sh --advisor -N 2 -n 18 -c 1    # 2 nodes, 18 ranks, 1 thread each
-./submit.sh --advisor -N 9 -n 18 -c 1    # 9 nodes, 18 ranks, 1 thread each
-./submit.sh --advisor -N 18 -n 18 -c 1   # 18 nodes, 18 ranks, 1 thread each
-```
-
-**Intel Advisor Weak Scaling Analysis:**
-- **Memory Bandwidth Scaling**: Track memory access patterns as problem size increases
-- **Cache Efficiency**: Monitor cache hit rates with larger datasets
-- **Vectorization Efficiency**: Ensure vectorization remains effective with scaling
-- **Load Balancing**: Identify task distribution imbalances across ranks
-- **Communication Overhead**: Analyze MPI communication costs relative to computation
-
-#### Intel Advisor Configuration Details
-
-**Environment Setup for Advisor Analysis:**
-```bash
-# Advisor-specific environment variables (set in run_advisor.sh)
-export ADVIXE_EXPERIMENTAL=roofline
-export ADVIXE_RUNTOOL_OPTIONS="--enable-stack-stitching --enable-data-transfer-analysis"
+./submit.sh --run -N 2 -n 16 -c 1 -p cosma   # 2 nodes, 16 ranks, 1 thread each
+./submit.sh --run -N 9 -n 16 -c 1 -p cosma   # 9 nodes, 16 ranks, 1 thread each
+./submit.sh --run -N 18 -n 16 -c 1 -p cosma  # 18 nodes, 16 ranks, 1 thread each
 ```
 
-**Analysis Workflow:**
-1. **Survey Collection**: Identify performance bottlenecks and hotspots
-2. **Tripcounts Collection**: Gather loop iteration information
-3. **Map Collection**: Analyze vectorization opportunities  
-4. **Dependencies Collection**: Identify optimization blockers
-5. **Roofline Analysis**: Compare achieved vs. theoretical performance
-
-**Output Analysis:**
-- **Threading Efficiency**: Compare single-threaded vs. multi-threaded performance
-- **Vectorization Ratio**: Percentage of vectorized vs. scalar operations
-- **Memory Bound vs. Compute Bound**: Identify limiting factors
-- **Scaling Efficiency**: Calculate parallel efficiency = (T₁ / (N × Tₙ)) × 100%
-- **Communication vs. Computation**: MPI overhead analysis
-
-**Expected Deliverables:**
-- Performance scaling curves for both strong and weak scaling
-- Vectorization efficiency reports across different configurations
-- Memory bandwidth utilization analysis
-- Task-based parallelism efficiency metrics
-- Recommendations for optimal resource allocation
-
-#### Analysis Integration with SWIFT's Task-Based Architecture
-
-Given SWIFT's task-based parallelism model, Intel Advisor will specifically analyze:
-
-- **Task Scheduler Efficiency**: How well SWIFT's scheduler distributes tasks across available cores
-- **Task Granularity**: Optimal task sizes for vectorization and cache efficiency  
-- **Memory Access Patterns**: Cache reuse between dependent tasks
-- **Load Balancing**: Work distribution across MPI ranks and OpenMP threads
-- **Hybrid Parallelism**: Effectiveness of MPI + OpenMP combination
-
-This comprehensive analysis will provide insights into SWIFT's performance characteristics on both traditional (COSMA) and high-core-count (COSMA5) architectures, guiding optimization strategies for large-scale human mobility simulations.
\ No newline at end of file
+_Note: Ideally, 18 MPI ranks are needed for an accurate weak scaling estimate. However, the older version of Cosma is limited to a maximum of 16 cores per node._
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index de2c34821..ece949c32 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -4,43 +4,51 @@
 HUMANS=humans-rivers-3
 RIVERS=river-rivers-3
 HUMANMOBILITY=humanMobility
-DATA=data-rivers-3
-IMAGES=images-rivers-3
+DATA=data-rivers-3-3-21
+IMAGES=images-rivers-3-3-21
+
+# Create the data directory if it doesn't exist
+mkdir -p ${DATA}
 
 # Render the YAML config from template
 export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-# Run SWIFT with SLURM environment variables
-# mpirun -np ${SLURM_NTASKS:-8} swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
-# likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g MEM -- \
-
-# likwid-mpirun -np ${SLURM_NTASKS:-8} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g FLOPS_DP -- \
-#     swift_mpi --threads=${SLURM_CPUS_PER_TASK:-16} \
-#     -A -s -g -G \
-#     --hm-river \
-#     --hm-randomwalk \
-#     -n 10000 \
-#     ${HUMANMOBILITY}.yml
-
-#OMP_NUM_THREADS=4 mpirun -n 4 bin/bt-mz.B.x
-    # --number-processes=${SLURM_NTASKS:-8} \
-    # --envv_OMP_NUM_THREADS=4 -- bin/bt-mz.B.x
-#maqao oneview -R1 --mpi-command="mpirun -np ${SLURM_NTASKS:-8}" \
-
-# module list
-
-# gdb --args swift -g --threads=4 -n 10000 ${HUMANMOBILITY}.yml # -A -s
-# swift_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
-#     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
-# mpirun -np ${SLURM_NTASKS:-8} \
-#     swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
-#     -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
-# likwid-perfctr -f -C 0 -g MEMREAD swift -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 100 ${HUMANMOBILITY}.yml
-likwid-perfctr -f -C 0 -g MEM swift_intel2025 -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
-# likwid-perfctr -f -C 0 -g FLOPS_DP swift -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
-# swift_intel2025 -h | grep version
-# likwid-mpirun -np ${SLURM_NTASKS:-1} -t ${SLURM_CPUS_PER_TASK:-16} -omp intel -g MEM -- \ # FLOPS_DP
-    # swift_mpi_intel2025 --threads=${SLURM_CPUS_PER_TASK:-16} \
-    # -A -s -g -G --hm-river --hm-randomwalk -n 10000 ${HUMANMOBILITY}.yml
-# likwid-perfctr -a
+envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Automatic selection between serial and parallel based on SLURM parameters
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "Auto-detecting execution mode:"
+echo "  SLURM_NTASKS: $ntasks"
+echo "  SLURM_CPUS_PER_TASK: $cpus_per_task"
+echo "  Output directory: ${DATA}"
+
+# Change to the data directory so all output files are written there
+cd ${DATA}
+
+if [ $ntasks -eq 1 ]; then
+    echo "Running SERIAL version (1 MPI rank)"
+    export OMP_NUM_THREADS=$cpus_per_task
+    
+    ${SWIFT} --threads=$cpus_per_task \
+        --task-dumps=10 -v 1 \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+else
+    echo "Running PARALLEL version ($ntasks MPI ranks)"
+    export OMP_NUM_THREADS=$cpus_per_task
+    
+    mpirun -n $ntasks \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        --task-dumps=10 -v 1 \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+fi
+
+echo "Simulation complete. Check output files and SWIFT task logs for analysis in ${DATA}/"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_advisor.sh b/examples/HumanMobility/run_advisor.sh
new file mode 100755
index 000000000..162443872
--- /dev/null
+++ b/examples/HumanMobility/run_advisor.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-advisor
+RIVERS=river-advisor
+HUMANMOBILITY=humanMobility
+DATA=data-advisor
+IMAGES=images-advisor
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "Intel Advisor Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Run Advisor with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running Intel Advisor on SERIAL SWIFT"
+    
+    echo "Running Intel Advisor survey analysis..."
+    advisor --collect=survey --project-dir=advisor_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Advisor survey report..."
+    advisor --report=survey --project-dir=advisor_intranode --format=text > advisor_survey_report.txt
+
+    echo "Running Intel Advisor tripcounts analysis..."
+    advisor --collect=tripcounts --project-dir=advisor_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Advisor tripcounts report..."
+    advisor --report=tripcounts --project-dir=advisor_intranode --format=text > advisor_tripcounts_report.txt
+
+    echo "Running Intel Advisor map analysis for vectorization opportunities..."
+    advisor --collect=map --project-dir=advisor_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Advisor map report..."
+    advisor --report=map --project-dir=advisor_intranode --format=text > advisor_map_report.txt
+else
+    echo "Running Intel Advisor on PARALLEL SWIFT"
+    
+    echo "Running Intel Advisor MPI survey analysis..."
+    srun -n $ntasks -c $cpus_per_task \
+        advisor --collect=survey --project-dir=advisor_mpi_intranode \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Advisor MPI survey report..."
+    advisor --report=survey --project-dir=advisor_mpi_intranode --format=text > advisor_mpi_survey_report.txt
+fi
+
+echo "Intel Advisor analysis complete. Results in advisor_*_report.txt and advisor_*_intranode/ directory"
+echo "To view interactive results, use: advisor-gui advisor_*_intranode"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_aps.sh b/examples/HumanMobility/run_aps.sh
new file mode 100755
index 000000000..aea948c63
--- /dev/null
+++ b/examples/HumanMobility/run_aps.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-aps
+RIVERS=river-aps
+HUMANMOBILITY=humanMobility
+DATA=data-aps
+IMAGES=images-aps
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "Intel APS Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+
+# Enable SWIFT's built-in logging
+# export SWIFT_TASK_DUMPS=1
+# export SWIFT_MPIUSE_REPORTS=1
+# export SWIFT_MEMUSE_REPORTS=1
+
+# Run APS with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running Intel APS on SERIAL SWIFT"
+    
+    echo "Running Intel APS performance snapshot analysis..."
+    aps -r aps_serial_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml #--task-dumps=1
+
+    echo "Generating APS summary report..."
+    aps --report=summary --result-dir=aps_serial_intranode > aps_serial_summary.txt
+
+    echo "Generating APS detailed report..."
+    aps --report=detailed --result-dir=aps_serial_intranode > aps_serial_detailed.txt
+else
+    echo "Running Intel APS on PARALLEL SWIFT"
+    
+    echo "Running Intel APS MPI performance snapshot analysis..."
+    srun -n $ntasks -c $cpus_per_task \
+        aps -r aps_mpi_intranode \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml #--task-dumps=1
+
+    echo "Generating APS MPI summary report..."
+    aps --report=summary --result-dir=aps_mpi_intranode > aps_mpi_summary.txt
+
+    echo "Generating APS MPI detailed report..."
+    aps --report=detailed --result-dir=aps_mpi_intranode > aps_mpi_detailed.txt
+fi
+
+echo "Intel APS analysis complete. Results in aps_*_summary.txt and aps_*_detailed.txt files"
+echo "Result directories: aps_*_intranode/"
+echo ""
+echo "Key APS metrics to check:"
+echo "  - CPU utilization and efficiency"
+echo "  - Memory bandwidth utilization"
+echo "  - Elapsed time and performance characteristics"
+echo "  - MPI communication overhead (for parallel runs)"
+echo ""
+echo "To view results:"
+echo "  cat aps_*_summary.txt     # Quick overview"
+echo "  cat aps_*_detailed.txt    # Detailed analysis"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_cosma.sh b/examples/HumanMobility/run_cosma.sh
new file mode 100755
index 000000000..26dbfb754
--- /dev/null
+++ b/examples/HumanMobility/run_cosma.sh
@@ -0,0 +1,54 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-rivers-3
+RIVERS=river-rivers-3
+HUMANMOBILITY=humanMobility
+DATA=data-rivers-1-2-8-cosma
+IMAGES=images-rivers-1-2-8-cosma
+
+# Create the data directory if it doesn't exist
+mkdir -p ${DATA}
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_cosma
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_cosma
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Automatic selection between serial and parallel based on SLURM parameters
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "Auto-detecting execution mode:"
+echo "  SLURM_NTASKS: $ntasks"
+echo "  SLURM_CPUS_PER_TASK: $cpus_per_task"
+echo "  Output directory: ${DATA}"
+
+# Change to the data directory so all output files are written there
+cd ${DATA}
+
+if [ $ntasks -eq 1 ]; then
+    echo "Running SERIAL version (1 MPI rank)"
+    export OMP_NUM_THREADS=$cpus_per_task
+    
+    ${SWIFT} --threads=$cpus_per_task \
+        --task-dumps=10 -v 1 \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+else
+    echo "Running PARALLEL version ($ntasks MPI ranks)"
+    export OMP_NUM_THREADS=$cpus_per_task
+    
+    mpirun -n $ntasks \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        --task-dumps=10 -v 1 \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+fi
+
+echo "Simulation complete. Check output files and SWIFT task logs for analysis in ${DATA}/"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_inspector.sh b/examples/HumanMobility/run_inspector.sh
new file mode 100755
index 000000000..de2d58355
--- /dev/null
+++ b/examples/HumanMobility/run_inspector.sh
@@ -0,0 +1,63 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-inspector
+RIVERS=river-inspector
+HUMANMOBILITY=humanMobility
+DATA=data-inspector
+IMAGES=images-inspector
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "Intel Inspector Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Run Inspector with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running Intel Inspector on SERIAL SWIFT"
+    
+    echo "Running Intel Inspector memory error analysis..."
+    inspxe-cl -collect mi2 -result-dir inspector_memory_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Inspector memory report..."
+    inspxe-cl -report summary -result-dir inspector_memory_intranode > inspector_memory_summary.txt
+
+    echo "Running Intel Inspector threading error analysis..."
+    inspxe-cl -collect ti2 -result-dir inspector_threading_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Inspector threading report..."
+    inspxe-cl -report summary -result-dir inspector_threading_intranode > inspector_threading_summary.txt
+else
+    echo "Running Intel Inspector on PARALLEL SWIFT"
+    
+    echo "Running Intel Inspector MPI memory error analysis..."
+    srun -n $ntasks -c $cpus_per_task \
+        inspxe-cl -collect mi2 -result-dir inspector_mpi_memory_intranode \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating Inspector MPI memory report..."
+    inspxe-cl -report summary -result-dir inspector_mpi_memory_intranode > inspector_mpi_memory_summary.txt
+fi
+
+echo "Intel Inspector analysis complete. Results in inspector_*_summary.txt and inspector_*_intranode/ directories"
+echo "To view interactive results, use: inspxe-gui inspector_*_intranode"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_likwid.sh b/examples/HumanMobility/run_likwid.sh
index 92e36021a..e7d8bd4ca 100755
--- a/examples/HumanMobility/run_likwid.sh
+++ b/examples/HumanMobility/run_likwid.sh
@@ -1,35 +1,77 @@
 #!/bin/bash
-#SBATCH --ntasks=1             # Total number of MPI tasks (cores) (max 16)
-#SBATCH --nodes=1              # Number of nodes
-#SBATCH --ntasks-per-node=1    # MPI tasks per node (max 16)
-#SBATCH --cpus-per-task=16     # CPU cores per MPI rank
-#SBATCH --mem=120G             # Memory per node
-#SBATCH -p cosma               # COSMA5 partition
-#SBATCH -A durham              # Account
-#SBATCH -t 2-00:00:00
-#SBATCH --mail-type=END
-#SBATCH --mail-user=lcgk69@durham.ac.uk
-#SBATCH --job-name=hm-perf-both
-#SBATCH --output=hm-perf-both.out
-#SBATCH --error=hm-perf-both.err
-
-# Load required modules
-module purge
-module load cosma
-module load intel_comp/2025.0.1
-module load umf compiler-rt tbb compiler mpi
-module load python
-module load fftw/3.3.10
-module load gsl
-module load parmetis/4.0.3-64bit
-module load parallel_hdf5/1.14.4
-module load sundials/5.8.0_c8_single
-module load likwid/5.4.1
-
-module list
-
-echo "=== Running MEM benchmark ==="
-./run-mem.sh
-
-echo "=== Running FLOPS_DP benchmark ==="
-./run-flops-dp.sh
\ No newline at end of file
+
+# Basenames for this run (without extensions)
+HUMANS=humans-likwid
+RIVERS=river-likwid
+HUMANMOBILITY=humanMobility
+DATA=data-likwid
+IMAGES=images-likwid
+
+# Create the data directory if it doesn't exist
+mkdir -p ${DATA}
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "LIKWID Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+echo "  Output directory: ${DATA}"
+
+# Change to the data directory so all output files are written there
+cd ${DATA}
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Run LIKWID with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running LIKWID on SERIAL SWIFT"
+    
+    echo "=== Running LIKWID MEM benchmark ==="
+    likwid-perfctr -C 0-$((cpus_per_task-1)) -g MEM \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+        > likwid_mem_summary.txt 2>&1
+
+    echo "=== Running LIKWID FLOPS_DP benchmark ==="
+    likwid-perfctr -C 0-$((cpus_per_task-1)) -g FLOPS_DP \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+        > likwid_flops_summary.txt 2>&1
+
+    echo "=== Running LIKWID L3 Cache benchmark ==="
+    likwid-perfctr -C 0-$((cpus_per_task-1)) -g L3 \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+        > likwid_cache_summary.txt 2>&1
+else
+    echo "Running LIKWID on PARALLEL SWIFT"
+    
+    echo "=== Running LIKWID MEM benchmark (MPI) ==="
+    srun -n $ntasks -c $cpus_per_task \
+        likwid-mpirun -np $ntasks -g MEM \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+        > likwid_mpi_mem_summary.txt 2>&1
+
+    echo "=== Running LIKWID FLOPS_DP benchmark (MPI) ==="
+    srun -n $ntasks -c $cpus_per_task \
+        likwid-mpirun -np $ntasks -g FLOPS_DP \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+        > likwid_mpi_flops_summary.txt 2>&1
+fi
+
+echo "LIKWID analysis complete. Results in likwid_*_summary.txt files"
+echo "Check the generated summary files for detailed performance metrics"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_maqao.sh b/examples/HumanMobility/run_maqao.sh
index ce37e974a..2740232fc 100755
--- a/examples/HumanMobility/run_maqao.sh
+++ b/examples/HumanMobility/run_maqao.sh
@@ -14,17 +14,36 @@ envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
 SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
 SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
 
-#OMP_NUM_THREADS=4 mpirun -n 4 bin/bt-mz.B.x
-    # --number-processes=${SLURM_NTASKS:-8} \
-    # --envv_OMP_NUM_THREADS=4 -- bin/bt-mz.B.x
-#maqao oneview -R1 --mpi-command="mpirun -np ${SLURM_NTASKS:-8}" \
-
-# Run Maqao oneview analysis on SWIFT (single node, threaded)
-# maqao oneview -R1 --output-format=all -- \
-#     ${SWIFT} -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-16} -n 1000 ${HUMANMOBILITY}.yml
-
-# Run Maqao oneview analysis on SWIFT MPI version with correct options
-maqao oneview -R1 --output-format=all \
-    --mpi-command="mpirun -np ${SLURM_NTASKS:-4}" \
-    --envv_OMP_NUM_THREADS="${SLURM_CPUS_PER_TASK:-4}" -- \
-    ${SWIFT_MPI} -A -s -g -G --hm-river --hm-randomwalk --threads=${SLURM_CPUS_PER_TASK:-4} -n 1000 ${HUMANMOBILITY}.yml
\ No newline at end of file
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "MAQAO Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+
+# Enable SWIFT's built-in MPI logging for detailed analysis
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Run MAQAO with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running MAQAO on SERIAL SWIFT"
+    maqao oneview -R1 --output-format=all \
+        --output-dir="maqao_serial_$(date +%Y-%m-%d_%H-%M-%S)" -- \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+else
+    echo "Running MAQAO on PARALLEL SWIFT"
+    maqao oneview -R1 --output-format=all \
+        --mpi-command="srun -n $ntasks -c $cpus_per_task" \
+        --envv_OMP_NUM_THREADS="$cpus_per_task" \
+        --envv_SWIFT_TASK_DUMPS="1" \
+        --envv_SWIFT_MPIUSE_REPORTS="1" \
+        --output-dir="maqao_mpi_$(date +%Y-%m-%d_%H-%M-%S)" -- \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+fi
+
+echo "MAQAO analysis complete. Check maqao_*/ directory for results."
diff --git a/examples/HumanMobility/run_scorep.sh b/examples/HumanMobility/run_scorep.sh
new file mode 100755
index 000000000..0a4771438
--- /dev/null
+++ b/examples/HumanMobility/run_scorep.sh
@@ -0,0 +1,67 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-scorep
+RIVERS=river-scorep
+HUMANMOBILITY=humanMobility
+DATA=data-scorep
+IMAGES=images-scorep
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "Score-P Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+echo "Running Score-P analysis..."
+
+# Set Score-P environment variables
+export SCOREP_ENABLE_PROFILING=true
+export SCOREP_ENABLE_TRACING=false
+export SCOREP_PROFILING_MAX_CALLPATH_DEPTH=30
+export SCOREP_TOTAL_MEMORY=1G
+
+# Note: For Score-P to work properly, SWIFT should be compiled with Score-P instrumentation
+echo "Warning: For full Score-P analysis, SWIFT should be recompiled with Score-P instrumentation"
+echo "Running with runtime instrumentation only..."
+
+# Run Score-P with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running Score-P on SERIAL SWIFT"
+    
+    ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+else
+    echo "Running Score-P on PARALLEL SWIFT"
+    
+    srun -n $ntasks -c $cpus_per_task \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+fi
+
+# Generate Score-P report if profile data exists
+if ls scorep-* 1> /dev/null 2>&1; then
+    echo "Generating Score-P summary report..."
+    scorep-score scorep-*/profile.cubex > scorep_intranode_summary.txt
+    
+    echo "Score-P analysis complete. Results in scorep_intranode_summary.txt and scorep-*/ directory"
+    echo "Use 'cube scorep-*/profile.cubex' for interactive analysis"
+else
+    echo "No Score-P profile data generated. Consider recompiling SWIFT with Score-P instrumentation."
+    echo "To enable full Score-P analysis, recompile SWIFT with:"
+    echo "  CC='scorep-gcc' CXX='scorep-g++' ./configure [options]"
+fi
\ No newline at end of file
diff --git a/examples/HumanMobility/run_vtune.sh b/examples/HumanMobility/run_vtune.sh
new file mode 100755
index 000000000..7102ef31a
--- /dev/null
+++ b/examples/HumanMobility/run_vtune.sh
@@ -0,0 +1,63 @@
+#!/bin/bash
+
+# Basenames for this run (without extensions)
+HUMANS=humans-vtune
+RIVERS=river-vtune
+HUMANMOBILITY=humanMobility
+DATA=data-vtune
+IMAGES=images-vtune
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
+
+SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+
+# Use SLURM environment variables for configuration
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+echo "VTune Analysis Configuration:"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+
+# Enable SWIFT's built-in logging
+export SWIFT_TASK_DUMPS=1
+export SWIFT_MPIUSE_REPORTS=1
+export SWIFT_MEMUSE_REPORTS=1
+
+# Run VTune with automatic serial/parallel detection
+if [ $ntasks -eq 1 ]; then
+    echo "Running VTune on SERIAL SWIFT"
+    
+    echo "Running VTune hotspots analysis..."
+    vtune -collect hotspots -result-dir vtune_hotspots_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating VTune hotspots summary report..."
+    vtune -report summary -result-dir vtune_hotspots_intranode > vtune_hotspots_summary.txt
+
+    echo "Running VTune threading analysis..."
+    vtune -collect threading -result-dir vtune_threading_intranode \
+        ${SWIFT} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating VTune threading report..."
+    vtune -report summary -result-dir vtune_threading_intranode > vtune_threading_summary.txt
+else
+    echo "Running VTune on PARALLEL SWIFT"
+    
+    echo "Running VTune MPI hotspots analysis..."
+    srun -n $ntasks -c $cpus_per_task \
+        vtune -collect hotspots -result-dir vtune_mpi_hotspots_intranode \
+        ${SWIFT_MPI} --threads=$cpus_per_task \
+        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+    echo "Generating VTune MPI summary report..."
+    vtune -report summary -result-dir vtune_mpi_hotspots_intranode > vtune_mpi_hotspots_summary.txt
+fi
+
+echo "VTune analysis complete. Results in vtune_*_summary.txt and vtune_*_intranode/ directories"
+echo "To view interactive results, use: vtune-gui vtune_*_intranode"
\ No newline at end of file
diff --git a/examples/HumanMobility/submit.sh b/examples/HumanMobility/submit.sh
index 0a832d760..0d0a7aedf 100755
--- a/examples/HumanMobility/submit.sh
+++ b/examples/HumanMobility/submit.sh
@@ -1,14 +1,91 @@
 #!/bin/bash
 
-if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--gen|--run|--vis|--map]"
+# Submit a job to run the Human Mobility example with specified mode and resources
+# ./submit.sh --run -N 1 -n 2 -c 64          # Run on COSMA5 (default, you can append "-p cosma5" optionally)
+# ./submit.sh --run -N 1 -n 2 -c 64 -p cosma # Run on COSMA (legacy hardware)
+# Note: The script can be extended to other partitions like Cosma7/Cosma8
+
+# Default values
+nodes=1
+ntasks=1
+cpus_per_task=64
+partition="cosma5"  # Default to cosma5, currently implemented for cosma5|cosma
+mode=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
+            mode=$1
+            shift
+            ;;
+        -N)
+            nodes="$2"
+            shift 2
+            ;;
+        -n)
+            ntasks="$2"
+            shift 2
+            ;;
+        -c)
+            cpus_per_task="$2"
+            shift 2
+            ;;
+        -p|--partition)
+            partition="$2"
+            shift 2
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Usage: $0 --TYPE [-N nodes] [-n ntasks] [-c cpus-per-task] [-p partition]"
+            echo "Types: gen, vis, map, run, likwid, maqao, aps, vtune, advisor, inspector, scorep"
+            echo "Partitions: cosma5 (default), cosma"
+            exit 1
+            ;;
+    esac
+done
+
+if [ -z "$mode" ]; then
+    echo "Usage: $0 --TYPE [-N nodes] [-n ntasks] [-c cpus-per-task] [-p partition]"
+    echo "Types: gen, vis, map, run, likwid, maqao, aps, vtune, advisor, inspector, scorep"
+    echo "Partitions: cosma5 (default), cosma"
     exit 1
 fi
 
-mode=$1
+# Validate partition
+case $partition in
+    cosma5|cosma)
+        ;;
+    *)
+        echo "Error: Invalid partition '$partition'. Use 'cosma5' or 'cosma'"
+        exit 1
+        ;;
+esac
+
 name=${mode#--}  # Remove leading -- from mode
 
+# Calculate memory per node (estimate: 4GB per core)
+mem_per_node=$((cpus_per_task * ntasks / nodes * 4))G
+
+# Select appropriate job script based on partition
+if [ "$partition" = "cosma" ]; then
+    job_script="job_cosma.sh"
+else
+    job_script="job.sh"
+fi
+
+echo "Submitting to partition: $partition"
+echo "Using job script: $job_script"
+echo "Resources: $nodes nodes, $ntasks tasks, $cpus_per_task cores/task"
+
+# Submit job with specified resources and partition
 sbatch --job-name="hm-$name" \
-       --output="hm-$name.out" \
-       --error="hm-$name.err" \
-       job.sh $mode
\ No newline at end of file
+       --output="hm-$name-$partition-rank%t.out" \
+       --error="hm-$name-$partition-rank%t.err" \
+       --partition="$partition" \
+       --nodes=$nodes \
+       --ntasks=$ntasks \
+       --ntasks-per-node=$((ntasks / nodes)) \
+       --cpus-per-task=$cpus_per_task \
+       --mem=$mem_per_node \
+       $job_script $mode
\ No newline at end of file

From b26d957603660851cd859745cd2a50d3179ea0cb Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Thu, 12 Jun 2025 19:32:19 +0100
Subject: [PATCH 39/43] Unified approach for job and run scripts with regard to
 partitions and performance analysis tools

Smaller number of files to maintain
Partition awareness (Cosma vs Cosma5))
Consistent directory naming
Automatic tool detection for performance analysis
Automatic SWIFT binary selection (intel2024 vs intel2025, serial vs parallel)
Documentation for SLURM job scheduling suite updated (including scenarios for weak scaling)
---
 examples/HumanMobility/job.sh                 |  43 ++-
 examples/HumanMobility/job_cosma.sh           | 103 -----
 examples/HumanMobility/perf/job_scheduling.md | 133 +++++--
 examples/HumanMobility/run.sh                 |  34 +-
 examples/HumanMobility/run_advisor.sh         |  71 ----
 examples/HumanMobility/run_aps.sh             |  71 ----
 examples/HumanMobility/run_cosma.sh           |  54 ---
 examples/HumanMobility/run_inspector.sh       |  63 ---
 examples/HumanMobility/run_likwid.sh          |  77 ----
 examples/HumanMobility/run_maqao.sh           |  49 ---
 examples/HumanMobility/run_perf.sh            | 365 ++++++++++++++++++
 examples/HumanMobility/run_scorep.sh          |  67 ----
 examples/HumanMobility/run_vtune.sh           |  63 ---
 examples/HumanMobility/submit.sh              |  13 +-
 14 files changed, 520 insertions(+), 686 deletions(-)
 delete mode 100644 examples/HumanMobility/job_cosma.sh
 delete mode 100755 examples/HumanMobility/run_advisor.sh
 delete mode 100755 examples/HumanMobility/run_aps.sh
 delete mode 100755 examples/HumanMobility/run_cosma.sh
 delete mode 100755 examples/HumanMobility/run_inspector.sh
 delete mode 100755 examples/HumanMobility/run_likwid.sh
 delete mode 100755 examples/HumanMobility/run_maqao.sh
 create mode 100755 examples/HumanMobility/run_perf.sh
 delete mode 100755 examples/HumanMobility/run_scorep.sh
 delete mode 100755 examples/HumanMobility/run_vtune.sh

diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index 1d8452464..7a44f1a50 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -1,10 +1,9 @@
 #!/bin/bash
-#SBATCH -p cosma5               # COSMA5 partition (with AVX support)
 #SBATCH -A durham               # Account
 #SBATCH -t 12:00:00             # Time limit hrs:min:sec
 #SBATCH --mail-type=END
 ##SBATCH --mail-user=<INSERT YOUR EMAIL ADDRESS>
-# Note: Some SLURM parameters are now passed from submit.sh, not hardcoded here
+# Note: SLURM partition and other parameters are passed from submit.sh
 
 # Check if an argument was provided
 if [ $# -ne 1 ]; then
@@ -23,19 +22,28 @@ case $mode in
         ;;
 esac
 
+# Detect partition and set hardware description
+if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+    hardware_desc="COSMA (legacy hardware)"
+    intel_comp_version="intel_comp/2024.2.0"  # Use older Intel compiler for compatibility
+else
+    hardware_desc="COSMA5 (modern hardware)"
+    intel_comp_version="intel_comp/2025.0.1"  # Use latest Intel compiler
+fi
+
 # Display current job configuration
 echo "Job Configuration:"
 echo "  Mode: $mode"
+echo "  Partition: $hardware_desc"
 echo "  Nodes: ${SLURM_JOB_NUM_NODES:-1}"
 echo "  MPI Tasks: ${SLURM_NTASKS:-1}"
 echo "  CPUs per Task: ${SLURM_CPUS_PER_TASK:-16}"
 echo "  Memory per Node: ${SLURM_MEM_PER_NODE:-64G}"
-echo "  Partition: COSMA5 (modern hardware)"
 
 # Common module loading
 module purge
 module load cosma
-module load intel_comp/2025.0.1 # gnu_comp/14.1.0 intel_comp/2024.2.0
+module load $intel_comp_version
 module load umf compiler-rt tbb compiler mpi
 # module load openmpi/5.0.3/
 
@@ -66,38 +74,47 @@ case $mode in
         ./run.sh
         ;;
     --likwid)
-		# Load likwid profiler
+        # Load likwid profiler
         module load likwid/5.4.1
-        ./run_likwid.sh
+        ./run_perf.sh --likwid
         ;;
     --maqao)
-		# Load Maqao profiler
+        # Load Maqao profiler
         module load maqao
-        ./run_maqao.sh
+        ./run_perf.sh --maqao
         ;;
     --aps)
+        if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+            echo "Warning: Intel APS 2025 may not be compatible with legacy COSMA hardware"
+        fi
         # Load APS profiler
         source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
-        ./run_aps.sh
+        ./run_perf.sh --aps
         ;;
     --vtune)
+        if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+            echo "Warning: Intel VTune 2025 may not be compatible with legacy COSMA hardware"
+        fi
         # Load VTune profiler
         source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
-        ./run_vtune.sh
+        ./run_perf.sh --vtune
         ;;
     --advisor)
+        if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+            echo "Warning: Intel Advisor 2025 may not be compatible with legacy COSMA hardware"
+        fi
         # Load Intel Advisor 2025.0
         source /cosma/local/intel/oneAPI_2025.0.1/advisor/2025.0/advisor-vars.sh
-        ./run_advisor.sh
+        ./run_perf.sh --advisor
         ;;
     --inspector)
         # Load Intel Inspector
         source /cosma/local/intel/oneAPI_2023.2.0/inspector/2023.2.0/inspxe-vars.sh
-        ./run_inspector.sh
+        ./run_perf.sh --inspector
         ;;
     --scorep)
         # Load Score-P for detailed instrumentation
         module load scorep/8.4
-        ./run_scorep.sh
+        ./run_perf.sh --scorep
         ;;
 esac
diff --git a/examples/HumanMobility/job_cosma.sh b/examples/HumanMobility/job_cosma.sh
deleted file mode 100644
index b230a1bc3..000000000
--- a/examples/HumanMobility/job_cosma.sh
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/bin/bash
-#SBATCH -p cosma                # COSMA partition (legacy)
-#SBATCH -A durham               # Account
-#SBATCH -t 12:00:00             # Time limit hrs:min:sec
-#SBATCH --mail-type=END
-##SBATCH --mail-user=<INSERT YOUR EMAIL ADDRESS>
-# Note: Some SLURM parameters are now passed from submit.sh, not hardcoded here
-
-# Check if an argument was provided
-if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep]"
-    exit 1
-fi
-
-mode=$1
-
-case $mode in
-    --gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
-        ;;
-    *)
-        echo "Invalid argument. Use --gen, --vis, --map, --run, --likwid, --maqao, --aps, --vtune, --advisor, --inspector, or --scorep"
-        exit 1
-        ;;
-esac
-
-# Display current job configuration
-echo "Job Configuration:"
-echo "  Mode: $mode"
-echo "  Partition: COSMA (legacy hardware)"
-echo "  Nodes: ${SLURM_JOB_NUM_NODES:-1}"
-echo "  MPI Tasks: ${SLURM_NTASKS:-1}"
-echo "  CPUs per Task: ${SLURM_CPUS_PER_TASK:-16}"
-echo "  Memory per Node: ${SLURM_MEM_PER_NODE:-64G}"
-
-# Common module loading - use older, compatible modules for COSMA
-module purge
-module load cosma
-module load intel_comp/2024.2.0    # Use older Intel compiler for compatibility
-module load compiler-rt tbb compiler mpi
-# module load openmpi/5.0.3/
-
-# Set MPI environment variables
-export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi.so
-export I_MPI_FABRICS=shm:ofi
-
-module load python
-module load fftw/3.3.10
-module load gsl
-module load parmetis/4.0.3-64bit
-module load parallel_hdf5/1.14.4
-module load sundials/5.8.0_c8_single
-
-# Load analysis-specific modules and execute the appropriate script
-case $mode in
-    --gen)
-        ./gen.sh
-        ;;
-    --vis)
-        module load ffmpeg
-        ./visualise.sh
-        ;;
-    --map)
-        ./map.sh
-        ;;
-    --run)
-        ./run_cosma.sh  # Use COSMA-compatible run script
-        ;;
-    --likwid)
-		# Load likwid profiler
-        module load likwid/5.4.1
-        ./run_likwid.sh
-        ;;
-    --maqao)
-		# Load Maqao profiler
-        module load maqao
-        ./run_maqao.sh
-        ;;
-    --aps)
-        echo "Warning: Intel APS 2025 may not be compatible with legacy COSMA hardware"
-        source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
-        ./run_aps.sh
-        ;;
-    --vtune)
-        echo "Warning: Intel VTune 2025 may not be compatible with legacy COSMA hardware"
-        source /cosma/local/intel/oneAPI_2025.0.1/vtune/2025.0/vtune-vars.sh
-        ./run_vtune.sh
-        ;;
-    --advisor)
-        echo "Warning: Intel Advisor 2025 may not be compatible with legacy COSMA hardware"
-        source /cosma/local/intel/oneAPI_2025.0.1/advisor/2025.0/advisor-vars.sh
-        ./run_advisor.sh
-        ;;
-    --inspector)
-        # Load Intel Inspector
-        source /cosma/local/intel/oneAPI_2023.2.0/inspector/2023.2.0/inspxe-vars.sh
-        ./run_inspector.sh
-        ;;
-    --scorep)
-        # Load Score-P for detailed instrumentation
-        module load scorep/8.4
-        ./run_scorep.sh
-        ;;
-esac
diff --git a/examples/HumanMobility/perf/job_scheduling.md b/examples/HumanMobility/perf/job_scheduling.md
index 8350faf23..bf27edfed 100644
--- a/examples/HumanMobility/perf/job_scheduling.md
+++ b/examples/HumanMobility/perf/job_scheduling.md
@@ -1,12 +1,12 @@
-# A SLURM job scheduling suite
+## A SLURM job scheduling suite
 
 ### Scripts
 
 Whenever you want to submit a job for a simulation (without or with profiling measurement) or for other applications (like visualisation), use the provided job submission script.
 
-* `./submit.sh --${type} -N ${nodes} -n ${ntasks} -c ${threads}` .. a job submission script to submit a job of choice with possible types to select from: `gen` .. to generate data of humans and a map of rivers; `vis` .. to plot simulation results; `map` .. to plot only geography without humans; a set of other options to run a simulation (`run` .. without proifiling; `likwid` .. profiled with _likwid_; `maqao` .. profiled with _Maqao_; `vtune` .. profiled with _Intel VTune_; `advisor` .. profiled with _Intel Advisor_; `inspector` .. profiled with _Intel Inspector_; `scorep` .. profiled with _Score-P_)
+* `./submit.sh --${type} -N ${nodes} -n ${ntasks} -c ${threads} [-p ${partition}]` .. a unified job submission script to submit a job of choice with possible types to select from: `gen` .. to generate data of humans and a map of rivers; `vis` .. to plot simulation results; `map` .. to plot only geography without humans; a set of other options to run a simulation (`run` .. without profiling; `likwid` .. profiled with _LIKWID_; `maqao` .. profiled with _MAQAO_; `aps` .. profiled with _Intel APS_; `vtune` .. profiled with _Intel VTune_; `advisor` .. profiled with _Intel Advisor_; `inspector` .. profiled with _Intel Inspector_; `scorep` .. profiled with _Score-P_)
 
-* `job.sh` .. a selector script to pass SLURM parameters further down to specific simulation scripts
+* `job.sh` .. a unified selector script that automatically detects partition and passes SLURM parameters to appropriate simulation scripts. Works on both COSMA5 and legacy COSMA partitions.
 
 * `gen.sh` .. a script to generate data of humans and a map of rivers (no parameters are passed)
 
@@ -14,27 +14,30 @@ Whenever you want to submit a job for a simulation (without or with profiling me
 
 * `map.sh` .. a script to plot only geography without humans (no parameters are passed)
 
-* `run.sh` .. a simulation script without profiling (with automatic selection of call mode between serial and parallel determined from additional parameters passed in `submit.sh`)
+* `run.sh` .. a unified simulation script without profiling that automatically detects partition (COSMA5 vs COSMA) and execution mode (serial vs parallel) based on SLURM parameters
 
-* `run_likwid.sh` .. a simulation script with profiling by means of _likwid_
+* `run_perf.sh` .. a unified performance analysis script that handles all profiling tools (`--likwid`, `--maqao`, `--aps`, `--vtune`, `--advisor`, `--inspector`, `--scorep`) with automatic partition detection and resource-based directory naming
 
-* `run_maqao.sh` .. a simulation script with profiling by means of _Maqao_
+#### Output Directory Structure
 
-* `run_vtune.sh` .. a simulation script with profiling by means of _Intel VTune_
+The unified scripts automatically generate descriptive directory names based on:
+- Tool used (for performance analysis)
+- Number of nodes, MPI tasks, and threads
+- Partition (COSMA5 vs COSMA)
 
-* `run_advisor.sh` .. a simulation script with profiling by means of _Intel Advisor_
+Examples:
+- `data-rivers-1-1-64` (basic run on COSMA5: 1 node, 1 task, 64 threads)
+- `data-rivers-1-1-16-cosma` (basic run on COSMA: 1 node, 1 task, 16 threads)
+- `data-likwid-2-4-32` (LIKWID analysis on COSMA5: 2 nodes, 4 tasks, 32 threads each)
+- `data-vtune-1-2-8-cosma` (VTune analysis on COSMA: 1 node, 2 tasks, 8 threads each)
 
-* `run_inspector.sh` .. a simulation script with profiling by means of _Intel Inspector_
+#### Usage examples
 
-* `run_scorep.sh` .. a simulation script with profiling by means of _Score-P_
-
-### Usage examples
-
-Basic usage with defaults (1 node, 1 MPI rank, 64 threads):
+Basic usage with defaults (1 node, 1 MPI rank, 64 threads on COSMA5):
 ```bash
-./submit.sh --run
-./submit.sh --maqao
-./submit.sh --vtune
+./submit.sh --run                     # Basic simulation
+./submit.sh --maqao                   # MAQAO performance analysis
+./submit.sh --vtune                   # Intel VTune profiling
 ```
 
 With custom resource allocation:
@@ -42,9 +45,34 @@ With custom resource allocation:
 ./submit.sh --likwid -N 1 -n 1 -c 32     # 1 node, 1 MPI rank, 32 threads
 ./submit.sh --advisor -N 1 -n 1 -c 16    # 1 node, 1 MPI rank, 16 threads
 ./submit.sh --inspector -N 1 -n 1 -c 8   # 1 node, 1 MPI rank, 8 threads
-./submit.sh --scorep -N 1 -n 4 -c 8      # 1 node, 4 MPI ranks, 8 threads
+./submit.sh --scorep -N 1 -n 4 -c 8      # 1 node, 4 MPI ranks, 8 threads each
+```
+
+Partition-specific usage:
+```bash
+# COSMA5 (modern hardware, default)
+./submit.sh --run -N 1 -n 1 -c 64        # Uses swift_intel2025 binary
+./submit.sh --run -N 1 -n 1 -c 64 -p cosma5  # Explicit partition specification
+
+# COSMA (legacy hardware)
+./submit.sh --run -N 1 -n 1 -c 16 -p cosma   # Uses swift_cosma binary, max 16 cores/node
+./submit.sh --vtune -N 2 -n 2 -c 8 -p cosma  # Distributed across legacy nodes
 ```
 
+#### Key Features of the Unified Approach
+
+* **Automatic partition detection**: Scripts automatically detect whether running on COSMA5 (modern) or COSMA (legacy) hardware and adapt accordingly
+* **Dynamic directory naming**: Output directories are automatically named based on tool, resources, and partition (e.g., `data-likwid-1-2-32-cosma`)
+* **Unified interface**: Same calling convention for all tools and partitions
+* **Resource-aware configuration**: Automatic selection of appropriate SWIFT binaries and compiler versions based on partition
+
+**Benefits of Unified Scripts:**
+- Single maintenance point for each functionality
+- Automatic adaptation to different hardware
+- Consistent naming scheme across all tools
+- Reduced complexity and improved maintainability
+- Future-proof design for new partitions (cosma7, cosma8, etc.)
+
 ### Performance analysis
 
 For strong scaling, I begin with a minimal simulation of human mobility on a square 100 x 100 humans on 10 x 10 km (for minimum of either 1000 steps or 10 minutes). For weak scaling, I'd like to begin on 2 ranks from a square 100 x 100 humans on 10 x 10 km and then on 8 and 18 ranks, increasing the problem size accordingly.
@@ -52,7 +80,6 @@ For strong scaling, I begin with a minimal simulation of human mobility on a squ
 To conduct performance analysis, we have the following queues:
 
 * The _cosma5_ queue is comprised of the new COSMA5 nodes, a total of 3 nodes each with 256 cores and 1.5TB RAM
-
 * The _cosma_ queue is comprised of the old COSMA nodes, a total of ~160 nodes each with 16 cores and 126GB RAM
 
 #### Strong scaling
@@ -91,37 +118,69 @@ For strong scaling analysis, we keep the problem size constant (100 x 100 humans
 ./submit.sh --run -N 4 -n 4 -c 4 -p cosma    # 4 old COSMA nodes, 4 MPI ranks, 4 threads each
 ```
 
+**Performance Analysis Integration:**
+```bash
+# Run the same configurations with different profiling tools
+./submit.sh --likwid -N 1 -n 1 -c 64    # LIKWID analysis on COSMA5
+./submit.sh --maqao -N 1 -n 1 -c 16 -p cosma  # MAQAO analysis on COSMA
+./submit.sh --vtune -N 2 -n 2 -c 128   # VTune analysis across 2 COSMA5 nodes
+```
+
 #### Weak scaling
 
 **Performance Analysis for Weak Scaling**
 
-For weak scaling, we maintain constant work per computational unit by increasing problem size proportionally with resources:
+For weak scaling, we maintain constant work per computational unit by increasing problem size proportionally with the number of nodes. For 2D problems like human mobility, the problem size should scale with the square root of the number of computing nodes.
+
+**Scaling Strategy:**
+- When we double the number of nodes, we should increase the linear dimension by ~1.414 (√2)
+- For example: 100×100 humans → 141×141 humans → 200×200 humans (approximately)
 
-**Baseline (2 MPI ranks, 100 x 100 humans):**
+**Baseline (2 nodes, 100 x 100 humans on 10 x 10 km):**
 ```bash
-./submit.sh --run -N 1 -n 2 -c 8     # Base: 100x100 humans, 2 ranks, 8 threads each
+./submit.sh --run -N 2 -n 2 -c 16 -p cosma  # 2 nodes, 2 ranks (1 per node), 16 threads each
+# or on COSMA5:
+./submit.sh --run -N 2 -n 2 -c 128          # 2 nodes, 2 ranks, 128 threads each
 ```
 
-**Scale to 8 MPI ranks (200 x 200 humans):**
+**Scale to 8 nodes (200 x 200 humans on 20 x 20 km):**
 ```bash
-# COSMA5 queue (high core count nodes)
-./submit.sh --run -N 1 -n 8 -c 32    # 1 node, 8 ranks, 32 threads each (256 total cores)
+./submit.sh --run -N 8 -n 8 -c 16 -p cosma  # 8 nodes, 8 ranks (1 per node), 16 threads each
+# or on COSMA5:
+./submit.sh --run -N 8 -n 8 -c 128          # 8 nodes, 8 ranks, 128 threads each
+```
 
-# COSMA queue (distributed across multiple nodes)
-./submit.sh --run -N 2 -n 8 -c 2 -p cosma    # 2 nodes, 8 ranks, 2 threads each
-./submit.sh --run -N 4 -n 8 -c 2 -p cosma    # 4 nodes, 8 ranks, 2 threads each  
-./submit.sh --run -N 8 -n 8 -c 2 -p cosma    # 8 nodes, 8 ranks, 2 threads each
+**Scale to 18 nodes (300 x 300 humans on 30 x 30 km):**
+```bash
+./submit.sh --run -N 18 -n 18 -c 16 -p cosma  # 18 nodes, 18 ranks (1 per node), 16 threads each
+# or on COSMA5:
+./submit.sh --run -N 18 -n 18 -c 128          # 18 nodes, 18 ranks, 128 threads each
 ```
 
-**Scale to 16 MPI ranks (300 x 300 humans):**
+**Important**: When submitting these jobs, you'll need to adjust your initial conditions file to match the problem size. Use the `makeIC.py` script with the appropriate `-n` parameter:
+
 ```bash
-# COSMA5 queue (maximum utilization)
-./submit.sh --run -N 1 -n 18 -c 14   # 1 node, 18 ranks, ~14 threads each
+# For 2 nodes (100x100 humans)
+python makeIC.py -n 100 -o humans-rivers-3.hdf5
 
-# COSMA queue (distributed)
-./submit.sh --run -N 2 -n 16 -c 1 -p cosma   # 2 nodes, 16 ranks, 1 thread each
-./submit.sh --run -N 9 -n 16 -c 1 -p cosma   # 9 nodes, 16 ranks, 1 thread each
-./submit.sh --run -N 18 -n 16 -c 1 -p cosma  # 18 nodes, 16 ranks, 1 thread each
+# For 8 nodes (200x200 humans)
+python makeIC.py -n 200 -o humans-rivers-3.hdf5
+
+# For 18 nodes (300x300 humans)
+python makeIC.py -n 300 -o humans-rivers-3.hdf5
 ```
 
-_Note: Ideally, 18 MPI ranks are needed for an accurate weak scaling estimate. However, the older version of Cosma is limited to a maximum of 16 cores per node._
+**Profiling at Scale:**
+```bash
+# Profile weak scaling performance at different node counts
+./submit.sh --likwid -N 2 -n 2 -c 16 -p cosma  # Baseline profiling (2 nodes)
+./submit.sh --likwid -N 8 -n 8 -c 16 -p cosma  # 8-node scaling analysis
+./submit.sh --vtune -N 18 -n 18 -c 16 -p cosma # 18-node analysis
+```
+
+**Expected Results:**
+- Execution time should remain approximately constant across all configurations
+- Memory usage per node should remain consistent
+- Communication overhead should grow modestly with node count
+
+_Note: For the most accurate weak scaling measurements, create separate initial condition files for each problem size and ensure the work per node remains constant across all configurations._
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index ece949c32..4f58be406 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -4,8 +4,21 @@
 HUMANS=humans-rivers-3
 RIVERS=river-rivers-3
 HUMANMOBILITY=humanMobility
-DATA=data-rivers-3-3-21
-IMAGES=images-rivers-3-3-21
+
+# Automatically generate directory names based on SLURM parameters
+nodes=${SLURM_JOB_NUM_NODES:-1}
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+# Determine partition suffix based on SLURM partition
+partition_suffix=""
+if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+    partition_suffix="-cosma"
+fi
+
+# Generate suffix based on resources and partition
+DATA=data-rivers-${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}
+IMAGES=images-rivers-${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}
 
 # Create the data directory if it doesn't exist
 mkdir -p ${DATA}
@@ -14,22 +27,27 @@ mkdir -p ${DATA}
 export DATA HUMANMOBILITY HUMANS RIVERS
 envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
 
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+# Select SWIFT binaries based on partition
+if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+    SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_cosma
+    SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_cosma
+else
+    SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+    SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+fi
 
 # Enable SWIFT's built-in logging
 export SWIFT_TASK_DUMPS=1
 export SWIFT_MPIUSE_REPORTS=1
 export SWIFT_MEMUSE_REPORTS=1
 
-# Automatic selection between serial and parallel based on SLURM parameters
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
 echo "Auto-detecting execution mode:"
+echo "  Partition: ${SLURM_JOB_PARTITION:-cosma5}"
+echo "  SLURM_JOB_NUM_NODES: $nodes"
 echo "  SLURM_NTASKS: $ntasks"
 echo "  SLURM_CPUS_PER_TASK: $cpus_per_task"
 echo "  Output directory: ${DATA}"
+echo "  SWIFT binary: $(basename ${SWIFT})"
 
 # Change to the data directory so all output files are written there
 cd ${DATA}
diff --git a/examples/HumanMobility/run_advisor.sh b/examples/HumanMobility/run_advisor.sh
deleted file mode 100755
index 162443872..000000000
--- a/examples/HumanMobility/run_advisor.sh
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-advisor
-RIVERS=river-advisor
-HUMANMOBILITY=humanMobility
-DATA=data-advisor
-IMAGES=images-advisor
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "Intel Advisor Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-
-# Enable SWIFT's built-in logging
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-# Run Advisor with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running Intel Advisor on SERIAL SWIFT"
-    
-    echo "Running Intel Advisor survey analysis..."
-    advisor --collect=survey --project-dir=advisor_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Advisor survey report..."
-    advisor --report=survey --project-dir=advisor_intranode --format=text > advisor_survey_report.txt
-
-    echo "Running Intel Advisor tripcounts analysis..."
-    advisor --collect=tripcounts --project-dir=advisor_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Advisor tripcounts report..."
-    advisor --report=tripcounts --project-dir=advisor_intranode --format=text > advisor_tripcounts_report.txt
-
-    echo "Running Intel Advisor map analysis for vectorization opportunities..."
-    advisor --collect=map --project-dir=advisor_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Advisor map report..."
-    advisor --report=map --project-dir=advisor_intranode --format=text > advisor_map_report.txt
-else
-    echo "Running Intel Advisor on PARALLEL SWIFT"
-    
-    echo "Running Intel Advisor MPI survey analysis..."
-    srun -n $ntasks -c $cpus_per_task \
-        advisor --collect=survey --project-dir=advisor_mpi_intranode \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Advisor MPI survey report..."
-    advisor --report=survey --project-dir=advisor_mpi_intranode --format=text > advisor_mpi_survey_report.txt
-fi
-
-echo "Intel Advisor analysis complete. Results in advisor_*_report.txt and advisor_*_intranode/ directory"
-echo "To view interactive results, use: advisor-gui advisor_*_intranode"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_aps.sh b/examples/HumanMobility/run_aps.sh
deleted file mode 100755
index aea948c63..000000000
--- a/examples/HumanMobility/run_aps.sh
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-aps
-RIVERS=river-aps
-HUMANMOBILITY=humanMobility
-DATA=data-aps
-IMAGES=images-aps
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "Intel APS Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-
-# Enable SWIFT's built-in logging
-# export SWIFT_TASK_DUMPS=1
-# export SWIFT_MPIUSE_REPORTS=1
-# export SWIFT_MEMUSE_REPORTS=1
-
-# Run APS with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running Intel APS on SERIAL SWIFT"
-    
-    echo "Running Intel APS performance snapshot analysis..."
-    aps -r aps_serial_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml #--task-dumps=1
-
-    echo "Generating APS summary report..."
-    aps --report=summary --result-dir=aps_serial_intranode > aps_serial_summary.txt
-
-    echo "Generating APS detailed report..."
-    aps --report=detailed --result-dir=aps_serial_intranode > aps_serial_detailed.txt
-else
-    echo "Running Intel APS on PARALLEL SWIFT"
-    
-    echo "Running Intel APS MPI performance snapshot analysis..."
-    srun -n $ntasks -c $cpus_per_task \
-        aps -r aps_mpi_intranode \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml #--task-dumps=1
-
-    echo "Generating APS MPI summary report..."
-    aps --report=summary --result-dir=aps_mpi_intranode > aps_mpi_summary.txt
-
-    echo "Generating APS MPI detailed report..."
-    aps --report=detailed --result-dir=aps_mpi_intranode > aps_mpi_detailed.txt
-fi
-
-echo "Intel APS analysis complete. Results in aps_*_summary.txt and aps_*_detailed.txt files"
-echo "Result directories: aps_*_intranode/"
-echo ""
-echo "Key APS metrics to check:"
-echo "  - CPU utilization and efficiency"
-echo "  - Memory bandwidth utilization"
-echo "  - Elapsed time and performance characteristics"
-echo "  - MPI communication overhead (for parallel runs)"
-echo ""
-echo "To view results:"
-echo "  cat aps_*_summary.txt     # Quick overview"
-echo "  cat aps_*_detailed.txt    # Detailed analysis"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_cosma.sh b/examples/HumanMobility/run_cosma.sh
deleted file mode 100755
index 26dbfb754..000000000
--- a/examples/HumanMobility/run_cosma.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-rivers-3
-RIVERS=river-rivers-3
-HUMANMOBILITY=humanMobility
-DATA=data-rivers-1-2-8-cosma
-IMAGES=images-rivers-1-2-8-cosma
-
-# Create the data directory if it doesn't exist
-mkdir -p ${DATA}
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_cosma
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_cosma
-
-# Enable SWIFT's built-in logging
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-# Automatic selection between serial and parallel based on SLURM parameters
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "Auto-detecting execution mode:"
-echo "  SLURM_NTASKS: $ntasks"
-echo "  SLURM_CPUS_PER_TASK: $cpus_per_task"
-echo "  Output directory: ${DATA}"
-
-# Change to the data directory so all output files are written there
-cd ${DATA}
-
-if [ $ntasks -eq 1 ]; then
-    echo "Running SERIAL version (1 MPI rank)"
-    export OMP_NUM_THREADS=$cpus_per_task
-    
-    ${SWIFT} --threads=$cpus_per_task \
-        --task-dumps=10 -v 1 \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
-else
-    echo "Running PARALLEL version ($ntasks MPI ranks)"
-    export OMP_NUM_THREADS=$cpus_per_task
-    
-    mpirun -n $ntasks \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        --task-dumps=10 -v 1 \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
-fi
-
-echo "Simulation complete. Check output files and SWIFT task logs for analysis in ${DATA}/"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_inspector.sh b/examples/HumanMobility/run_inspector.sh
deleted file mode 100755
index de2d58355..000000000
--- a/examples/HumanMobility/run_inspector.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-inspector
-RIVERS=river-inspector
-HUMANMOBILITY=humanMobility
-DATA=data-inspector
-IMAGES=images-inspector
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "Intel Inspector Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-
-# Enable SWIFT's built-in logging
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-# Run Inspector with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running Intel Inspector on SERIAL SWIFT"
-    
-    echo "Running Intel Inspector memory error analysis..."
-    inspxe-cl -collect mi2 -result-dir inspector_memory_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Inspector memory report..."
-    inspxe-cl -report summary -result-dir inspector_memory_intranode > inspector_memory_summary.txt
-
-    echo "Running Intel Inspector threading error analysis..."
-    inspxe-cl -collect ti2 -result-dir inspector_threading_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Inspector threading report..."
-    inspxe-cl -report summary -result-dir inspector_threading_intranode > inspector_threading_summary.txt
-else
-    echo "Running Intel Inspector on PARALLEL SWIFT"
-    
-    echo "Running Intel Inspector MPI memory error analysis..."
-    srun -n $ntasks -c $cpus_per_task \
-        inspxe-cl -collect mi2 -result-dir inspector_mpi_memory_intranode \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating Inspector MPI memory report..."
-    inspxe-cl -report summary -result-dir inspector_mpi_memory_intranode > inspector_mpi_memory_summary.txt
-fi
-
-echo "Intel Inspector analysis complete. Results in inspector_*_summary.txt and inspector_*_intranode/ directories"
-echo "To view interactive results, use: inspxe-gui inspector_*_intranode"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_likwid.sh b/examples/HumanMobility/run_likwid.sh
deleted file mode 100755
index e7d8bd4ca..000000000
--- a/examples/HumanMobility/run_likwid.sh
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-likwid
-RIVERS=river-likwid
-HUMANMOBILITY=humanMobility
-DATA=data-likwid
-IMAGES=images-likwid
-
-# Create the data directory if it doesn't exist
-mkdir -p ${DATA}
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "LIKWID Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-echo "  Output directory: ${DATA}"
-
-# Change to the data directory so all output files are written there
-cd ${DATA}
-
-# Enable SWIFT's built-in logging
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-# Run LIKWID with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running LIKWID on SERIAL SWIFT"
-    
-    echo "=== Running LIKWID MEM benchmark ==="
-    likwid-perfctr -C 0-$((cpus_per_task-1)) -g MEM \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
-        > likwid_mem_summary.txt 2>&1
-
-    echo "=== Running LIKWID FLOPS_DP benchmark ==="
-    likwid-perfctr -C 0-$((cpus_per_task-1)) -g FLOPS_DP \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
-        > likwid_flops_summary.txt 2>&1
-
-    echo "=== Running LIKWID L3 Cache benchmark ==="
-    likwid-perfctr -C 0-$((cpus_per_task-1)) -g L3 \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
-        > likwid_cache_summary.txt 2>&1
-else
-    echo "Running LIKWID on PARALLEL SWIFT"
-    
-    echo "=== Running LIKWID MEM benchmark (MPI) ==="
-    srun -n $ntasks -c $cpus_per_task \
-        likwid-mpirun -np $ntasks -g MEM \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
-        > likwid_mpi_mem_summary.txt 2>&1
-
-    echo "=== Running LIKWID FLOPS_DP benchmark (MPI) ==="
-    srun -n $ntasks -c $cpus_per_task \
-        likwid-mpirun -np $ntasks -g FLOPS_DP \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
-        > likwid_mpi_flops_summary.txt 2>&1
-fi
-
-echo "LIKWID analysis complete. Results in likwid_*_summary.txt files"
-echo "Check the generated summary files for detailed performance metrics"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_maqao.sh b/examples/HumanMobility/run_maqao.sh
deleted file mode 100755
index 2740232fc..000000000
--- a/examples/HumanMobility/run_maqao.sh
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-maqao
-RIVERS=river-maqao
-HUMANMOBILITY=humanMobility
-DATA=data-maqao
-IMAGES=images-maqao
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "MAQAO Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-
-# Enable SWIFT's built-in MPI logging for detailed analysis
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-# Run MAQAO with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running MAQAO on SERIAL SWIFT"
-    maqao oneview -R1 --output-format=all \
-        --output-dir="maqao_serial_$(date +%Y-%m-%d_%H-%M-%S)" -- \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-else
-    echo "Running MAQAO on PARALLEL SWIFT"
-    maqao oneview -R1 --output-format=all \
-        --mpi-command="srun -n $ntasks -c $cpus_per_task" \
-        --envv_OMP_NUM_THREADS="$cpus_per_task" \
-        --envv_SWIFT_TASK_DUMPS="1" \
-        --envv_SWIFT_MPIUSE_REPORTS="1" \
-        --output-dir="maqao_mpi_$(date +%Y-%m-%d_%H-%M-%S)" -- \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-fi
-
-echo "MAQAO analysis complete. Check maqao_*/ directory for results."
diff --git a/examples/HumanMobility/run_perf.sh b/examples/HumanMobility/run_perf.sh
new file mode 100755
index 000000000..96c8ce5f8
--- /dev/null
+++ b/examples/HumanMobility/run_perf.sh
@@ -0,0 +1,365 @@
+#!/bin/bash
+
+# Unified performance analysis script for SWIFT Human Mobility simulations
+# Usage: ./run_perf.sh --TOOL
+# where TOOL can be: likwid, maqao, aps, vtune, advisor, inspector, scorep
+
+# Check if an argument was provided
+if [ $# -ne 1 ]; then
+    echo "Usage: $0 [--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep]"
+    exit 1
+fi
+
+mode=$1
+
+case $mode in
+    --likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
+        ;;
+    *)
+        echo "Invalid argument. Use --likwid, --maqao, --aps, --vtune, --advisor, --inspector, or --scorep"
+        exit 1
+        ;;
+esac
+
+# Extract tool name from argument
+TOOL=${mode#--}
+
+# Basenames for this run (without extensions)
+HUMANS=humans-${TOOL}
+RIVERS=river-${TOOL}
+HUMANMOBILITY=humanMobility
+
+# Automatically generate directory names based on SLURM parameters
+nodes=${SLURM_JOB_NUM_NODES:-1}
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
+# Determine partition suffix
+partition_suffix=""
+if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+    partition_suffix="-cosma"
+fi
+
+# Generate suffix based on resources and tool
+DATA=data-${TOOL}-${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}
+IMAGES=images-${TOOL}-${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}
+
+# Create the data directory if it doesn't exist
+mkdir -p ${DATA}
+
+# Render the YAML config from template
+export DATA HUMANMOBILITY HUMANS RIVERS
+envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
+
+# Select SWIFT binaries based on partition
+if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+    SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_cosma
+    SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_cosma
+else
+    SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
+    SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
+fi
+
+echo "${TOOL^^} Analysis Configuration:"
+echo "  Tool: $TOOL"
+echo "  Partition: ${SLURM_JOB_PARTITION:-cosma5}"
+echo "  MPI Tasks: $ntasks"
+echo "  OpenMP Threads per Task: $cpus_per_task"
+echo "  Output directory: ${DATA}"
+
+# Change to the data directory so all output files are written there
+cd ${DATA}
+
+# Enable SWIFT's built-in logging (disable for APS to avoid conflicts)
+if [[ "$TOOL" != "aps" ]]; then
+    export SWIFT_TASK_DUMPS=1
+    export SWIFT_MPIUSE_REPORTS=1
+    export SWIFT_MEMUSE_REPORTS=1
+fi
+
+# Tool-specific analysis functions
+run_likwid() {
+    if [ $ntasks -eq 1 ]; then
+        echo "Running LIKWID on SERIAL SWIFT"
+        
+        echo "=== Running LIKWID MEM benchmark ==="
+        likwid-perfctr -C 0-$((cpus_per_task-1)) -g MEM \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+            > likwid_mem_summary.txt 2>&1
+
+        echo "=== Running LIKWID FLOPS_DP benchmark ==="
+        likwid-perfctr -C 0-$((cpus_per_task-1)) -g FLOPS_DP \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+            > likwid_flops_summary.txt 2>&1
+
+        echo "=== Running LIKWID L3 Cache benchmark ==="
+        likwid-perfctr -C 0-$((cpus_per_task-1)) -g L3 \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+            > likwid_cache_summary.txt 2>&1
+    else
+        echo "Running LIKWID on PARALLEL SWIFT"
+        
+        echo "=== Running LIKWID MEM benchmark (MPI) ==="
+        srun -n $ntasks -c $cpus_per_task \
+            likwid-mpirun -np $ntasks -g MEM \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+            > likwid_mpi_mem_summary.txt 2>&1
+
+        echo "=== Running LIKWID FLOPS_DP benchmark (MPI) ==="
+        srun -n $ntasks -c $cpus_per_task \
+            likwid-mpirun -np $ntasks -g FLOPS_DP \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1 \
+            > likwid_mpi_flops_summary.txt 2>&1
+    fi
+    echo "LIKWID analysis complete. Results in likwid_*_summary.txt files"
+	echo "Check the generated summary files for detailed performance metrics"
+}
+
+run_maqao() {
+    if [ $ntasks -eq 1 ]; then
+        echo "Running MAQAO on SERIAL SWIFT"
+        maqao oneview -R1 --output-format=all \
+            --output-dir="maqao_serial_$(date +%Y-%m-%d_%H-%M-%S)" -- \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+    else
+        echo "Running MAQAO on PARALLEL SWIFT"
+        maqao oneview -R1 --output-format=all \
+            --mpi-command="srun -n $ntasks -c $cpus_per_task" \
+            --envv_OMP_NUM_THREADS="$cpus_per_task" \
+            --envv_SWIFT_TASK_DUMPS="1" \
+            --envv_SWIFT_MPIUSE_REPORTS="1" \
+            --output-dir="maqao_mpi_$(date +%Y-%m-%d_%H-%M-%S)" -- \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+    fi
+    echo "MAQAO analysis complete. Check maqao_*/ directory for results."
+}
+
+run_aps() {
+    if [ $ntasks -eq 1 ]; then
+        echo "Running Intel APS on SERIAL SWIFT"
+        
+        echo "Running Intel APS performance snapshot analysis..."
+        aps -r aps_serial_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+
+        echo "Generating APS summary report..."
+        aps --report=summary --result-dir=aps_serial_intranode > aps_serial_summary.txt
+
+        echo "Generating APS detailed report..."
+        aps --report=detailed --result-dir=aps_serial_intranode > aps_serial_detailed.txt
+    else
+        echo "Running Intel APS on PARALLEL SWIFT"
+        
+        echo "Running Intel APS MPI performance snapshot analysis..."
+        srun -n $ntasks -c $cpus_per_task \
+            aps -r aps_mpi_intranode \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+
+        echo "Generating APS MPI summary report..."
+        aps --report=summary --result-dir=aps_mpi_intranode > aps_mpi_summary.txt
+
+        echo "Generating APS MPI detailed report..."
+        aps --report=detailed --result-dir=aps_mpi_intranode > aps_mpi_detailed.txt
+    fi
+    echo "Intel APS analysis complete. Results in aps_*_summary.txt and aps_*_detailed.txt files"
+	echo "Result directories: aps_*_intranode/"
+	echo ""
+	echo "Key APS metrics to check:"
+	echo "  - CPU utilization and efficiency"
+	echo "  - Memory bandwidth utilization"
+	echo "  - Elapsed time and performance characteristics"
+	echo "  - MPI communication overhead (for parallel runs)"
+	echo ""
+	echo "To view results:"
+	echo "  cat aps_*_summary.txt     # Quick overview"
+	echo "  cat aps_*_detailed.txt    # Detailed analysis"
+}
+
+run_vtune() {
+    if [ $ntasks -eq 1 ]; then
+        echo "Running VTune on SERIAL SWIFT"
+        
+        echo "Running VTune hotspots analysis..."
+        vtune -collect hotspots -result-dir vtune_hotspots_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating VTune hotspots summary report..."
+        vtune -report summary -result-dir vtune_hotspots_intranode > vtune_hotspots_summary.txt
+
+        echo "Running VTune threading analysis..."
+        vtune -collect threading -result-dir vtune_threading_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating VTune threading report..."
+        vtune -report summary -result-dir vtune_threading_intranode > vtune_threading_summary.txt
+    else
+        echo "Running VTune on PARALLEL SWIFT"
+        
+        echo "Running VTune MPI hotspots analysis..."
+        srun -n $ntasks -c $cpus_per_task \
+            vtune -collect hotspots -result-dir vtune_mpi_hotspots_intranode \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating VTune MPI summary report..."
+        vtune -report summary -result-dir vtune_mpi_hotspots_intranode > vtune_mpi_hotspots_summary.txt
+    fi
+    echo "VTune analysis complete. Results in vtune_*_summary.txt and vtune_*_intranode/ directories"
+	echo "To view interactive results, use: vtune-gui vtune_*_intranode"
+}
+
+run_advisor() {
+    if [ $ntasks -eq 1 ]; then
+        echo "Running Intel Advisor on SERIAL SWIFT"
+        
+        echo "Running Intel Advisor survey analysis..."
+        advisor --collect=survey --project-dir=advisor_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Advisor survey report..."
+        advisor --report=survey --project-dir=advisor_intranode --format=text > advisor_survey_report.txt
+
+        echo "Running Intel Advisor tripcounts analysis..."
+        advisor --collect=tripcounts --project-dir=advisor_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Advisor tripcounts report..."
+        advisor --report=tripcounts --project-dir=advisor_intranode --format=text > advisor_tripcounts_report.txt
+
+        echo "Running Intel Advisor map analysis for vectorization opportunities..."
+        advisor --collect=map --project-dir=advisor_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Advisor map report..."
+        advisor --report=map --project-dir=advisor_intranode --format=text > advisor_map_report.txt
+    else
+        echo "Running Intel Advisor on PARALLEL SWIFT"
+        
+        echo "Running Intel Advisor MPI survey analysis..."
+        srun -n $ntasks -c $cpus_per_task \
+            advisor --collect=survey --project-dir=advisor_mpi_intranode \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Advisor MPI survey report..."
+        advisor --report=survey --project-dir=advisor_mpi_intranode --format=text > advisor_mpi_survey_report.txt
+    fi
+    echo "Intel Advisor analysis complete. Results in advisor_*_report.txt and advisor_*_intranode/ directory"
+	echo "To view interactive results, use: advisor-gui advisor_*_intranode"
+}
+
+run_inspector() {
+    if [ $ntasks -eq 1 ]; then
+        echo "Running Intel Inspector on SERIAL SWIFT"
+        
+        echo "Running Intel Inspector memory error analysis..."
+        inspxe-cl -collect mi2 -result-dir inspector_memory_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Inspector memory report..."
+        inspxe-cl -report summary -result-dir inspector_memory_intranode > inspector_memory_summary.txt
+
+        echo "Running Intel Inspector threading error analysis..."
+        inspxe-cl -collect ti2 -result-dir inspector_threading_intranode \
+            ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Inspector threading report..."
+        inspxe-cl -report summary -result-dir inspector_threading_intranode > inspector_threading_summary.txt
+    else
+        echo "Running Intel Inspector on PARALLEL SWIFT"
+        
+        echo "Running Intel Inspector MPI memory error analysis..."
+        srun -n $ntasks -c $cpus_per_task \
+            inspxe-cl -collect mi2 -result-dir inspector_mpi_memory_intranode \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+
+        echo "Generating Inspector MPI memory report..."
+        inspxe-cl -report summary -result-dir inspector_mpi_memory_intranode > inspector_mpi_memory_summary.txt
+    fi
+    echo "Intel Inspector analysis complete. Results in inspector_*_summary.txt and inspector_*_intranode/ directories"
+	echo "To view interactive results, use: inspxe-gui inspector_*_intranode"
+}
+
+run_scorep() {
+    echo "Running Score-P analysis..."
+
+    # Set Score-P environment variables
+    export SCOREP_ENABLE_PROFILING=true
+    export SCOREP_ENABLE_TRACING=false
+    export SCOREP_PROFILING_MAX_CALLPATH_DEPTH=30
+    export SCOREP_TOTAL_MEMORY=1G
+
+	# Note: For Score-P to work properly, SWIFT should be compiled with Score-P instrumentation
+    echo "Warning: For full Score-P analysis, SWIFT should be recompiled with Score-P instrumentation"
+    echo "Running with runtime instrumentation only..."
+
+    if [ $ntasks -eq 1 ]; then
+        echo "Running Score-P on SERIAL SWIFT"
+        
+        ${SWIFT} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+    else
+        echo "Running Score-P on PARALLEL SWIFT"
+        
+        srun -n $ntasks -c $cpus_per_task \
+            ${SWIFT_MPI} --threads=$cpus_per_task \
+            -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
+    fi
+
+    # Generate Score-P report if profile data exists
+    if ls scorep-* 1> /dev/null 2>&1; then
+        echo "Generating Score-P summary report..."
+        scorep-score scorep-*/profile.cubex > scorep_intranode_summary.txt
+        
+        echo "Score-P analysis complete. Results in scorep_intranode_summary.txt and scorep-*/ directory"
+        echo "Use 'cube scorep-*/profile.cubex' for interactive analysis"
+    else
+        echo "No Score-P profile data generated. Consider recompiling SWIFT with Score-P instrumentation."
+        echo "To enable full Score-P analysis, recompile SWIFT with:"
+        echo "  CC='scorep-gcc' CXX='scorep-g++' ./configure [options]"
+    fi
+}
+
+# Execute the appropriate analysis function
+case $TOOL in
+    likwid)
+        run_likwid
+        ;;
+    maqao)
+        run_maqao
+        ;;
+    aps)
+        run_aps
+        ;;
+    vtune)
+        run_vtune
+        ;;
+    advisor)
+        run_advisor
+        ;;
+    inspector)
+        run_inspector
+        ;;
+    scorep)
+        run_scorep
+        ;;
+esac
+
+echo "Check the generated files for detailed performance metrics in ${DATA}/"
\ No newline at end of file
diff --git a/examples/HumanMobility/run_scorep.sh b/examples/HumanMobility/run_scorep.sh
deleted file mode 100755
index 0a4771438..000000000
--- a/examples/HumanMobility/run_scorep.sh
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-scorep
-RIVERS=river-scorep
-HUMANMOBILITY=humanMobility
-DATA=data-scorep
-IMAGES=images-scorep
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "Score-P Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-
-# Enable SWIFT's built-in logging
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-echo "Running Score-P analysis..."
-
-# Set Score-P environment variables
-export SCOREP_ENABLE_PROFILING=true
-export SCOREP_ENABLE_TRACING=false
-export SCOREP_PROFILING_MAX_CALLPATH_DEPTH=30
-export SCOREP_TOTAL_MEMORY=1G
-
-# Note: For Score-P to work properly, SWIFT should be compiled with Score-P instrumentation
-echo "Warning: For full Score-P analysis, SWIFT should be recompiled with Score-P instrumentation"
-echo "Running with runtime instrumentation only..."
-
-# Run Score-P with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running Score-P on SERIAL SWIFT"
-    
-    ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-else
-    echo "Running Score-P on PARALLEL SWIFT"
-    
-    srun -n $ntasks -c $cpus_per_task \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-fi
-
-# Generate Score-P report if profile data exists
-if ls scorep-* 1> /dev/null 2>&1; then
-    echo "Generating Score-P summary report..."
-    scorep-score scorep-*/profile.cubex > scorep_intranode_summary.txt
-    
-    echo "Score-P analysis complete. Results in scorep_intranode_summary.txt and scorep-*/ directory"
-    echo "Use 'cube scorep-*/profile.cubex' for interactive analysis"
-else
-    echo "No Score-P profile data generated. Consider recompiling SWIFT with Score-P instrumentation."
-    echo "To enable full Score-P analysis, recompile SWIFT with:"
-    echo "  CC='scorep-gcc' CXX='scorep-g++' ./configure [options]"
-fi
\ No newline at end of file
diff --git a/examples/HumanMobility/run_vtune.sh b/examples/HumanMobility/run_vtune.sh
deleted file mode 100755
index 7102ef31a..000000000
--- a/examples/HumanMobility/run_vtune.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/bin/bash
-
-# Basenames for this run (without extensions)
-HUMANS=humans-vtune
-RIVERS=river-vtune
-HUMANMOBILITY=humanMobility
-DATA=data-vtune
-IMAGES=images-vtune
-
-# Render the YAML config from template
-export DATA HUMANMOBILITY HUMANS RIVERS
-envsubst < humanMobility_template.yml > ${HUMANMOBILITY}.yml
-
-SWIFT=/cosma5/data/durham/dc-niko3/.local/bin/swift_intel2025
-SWIFT_MPI=/cosma5/data/durham/dc-niko3/.local/bin/swift_mpi_intel2025
-
-# Use SLURM environment variables for configuration
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
-
-echo "VTune Analysis Configuration:"
-echo "  MPI Tasks: $ntasks"
-echo "  OpenMP Threads per Task: $cpus_per_task"
-
-# Enable SWIFT's built-in logging
-export SWIFT_TASK_DUMPS=1
-export SWIFT_MPIUSE_REPORTS=1
-export SWIFT_MEMUSE_REPORTS=1
-
-# Run VTune with automatic serial/parallel detection
-if [ $ntasks -eq 1 ]; then
-    echo "Running VTune on SERIAL SWIFT"
-    
-    echo "Running VTune hotspots analysis..."
-    vtune -collect hotspots -result-dir vtune_hotspots_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating VTune hotspots summary report..."
-    vtune -report summary -result-dir vtune_hotspots_intranode > vtune_hotspots_summary.txt
-
-    echo "Running VTune threading analysis..."
-    vtune -collect threading -result-dir vtune_threading_intranode \
-        ${SWIFT} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating VTune threading report..."
-    vtune -report summary -result-dir vtune_threading_intranode > vtune_threading_summary.txt
-else
-    echo "Running VTune on PARALLEL SWIFT"
-    
-    echo "Running VTune MPI hotspots analysis..."
-    srun -n $ntasks -c $cpus_per_task \
-        vtune -collect hotspots -result-dir vtune_mpi_hotspots_intranode \
-        ${SWIFT_MPI} --threads=$cpus_per_task \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml --task-dumps=1
-
-    echo "Generating VTune MPI summary report..."
-    vtune -report summary -result-dir vtune_mpi_hotspots_intranode > vtune_mpi_hotspots_summary.txt
-fi
-
-echo "VTune analysis complete. Results in vtune_*_summary.txt and vtune_*_intranode/ directories"
-echo "To view interactive results, use: vtune-gui vtune_*_intranode"
\ No newline at end of file
diff --git a/examples/HumanMobility/submit.sh b/examples/HumanMobility/submit.sh
index 0d0a7aedf..cc8a60e56 100755
--- a/examples/HumanMobility/submit.sh
+++ b/examples/HumanMobility/submit.sh
@@ -67,18 +67,11 @@ name=${mode#--}  # Remove leading -- from mode
 # Calculate memory per node (estimate: 4GB per core)
 mem_per_node=$((cpus_per_task * ntasks / nodes * 4))G
 
-# Select appropriate job script based on partition
-if [ "$partition" = "cosma" ]; then
-    job_script="job_cosma.sh"
-else
-    job_script="job.sh"
-fi
-
 echo "Submitting to partition: $partition"
-echo "Using job script: $job_script"
+echo "Using unified job script: job.sh"
 echo "Resources: $nodes nodes, $ntasks tasks, $cpus_per_task cores/task"
 
-# Submit job with specified resources and partition
+# Submit job with specified resources and partition - now always use job.sh
 sbatch --job-name="hm-$name" \
        --output="hm-$name-$partition-rank%t.out" \
        --error="hm-$name-$partition-rank%t.err" \
@@ -88,4 +81,4 @@ sbatch --job-name="hm-$name" \
        --ntasks-per-node=$((ntasks / nodes)) \
        --cpus-per-task=$cpus_per_task \
        --mem=$mem_per_node \
-       $job_script $mode
\ No newline at end of file
+       job.sh $mode
\ No newline at end of file

From 3fe2c3d6b3d77b097d524fac9d389146143aa8db Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 13 Jun 2025 23:12:57 +0100
Subject: [PATCH 40/43] Update visualisation of performance results and doc for
 job scheduling suite

---
 .../{perf => doc}/job_scheduling.md           |  89 +++++
 examples/HumanMobility/{perf => doc}/paws.md  |   0
 examples/HumanMobility/pa_vis.py              | 350 ++++++++++++------
 3 files changed, 330 insertions(+), 109 deletions(-)
 rename examples/HumanMobility/{perf => doc}/job_scheduling.md (74%)
 rename examples/HumanMobility/{perf => doc}/paws.md (100%)

diff --git a/examples/HumanMobility/perf/job_scheduling.md b/examples/HumanMobility/doc/job_scheduling.md
similarity index 74%
rename from examples/HumanMobility/perf/job_scheduling.md
rename to examples/HumanMobility/doc/job_scheduling.md
index bf27edfed..818b861c4 100644
--- a/examples/HumanMobility/perf/job_scheduling.md
+++ b/examples/HumanMobility/doc/job_scheduling.md
@@ -184,3 +184,92 @@ python makeIC.py -n 300 -o humans-rivers-3.hdf5
 - Communication overhead should grow modestly with node count
 
 _Note: For the most accurate weak scaling measurements, create separate initial condition files for each problem size and ensure the work per node remains constant across all configurations._
+
+### Visualisation of performance results
+
+The performance analysis results can be visualized using the `pa_vis.py` script, which creates comprehensive PDF reports showing memory usage, CPU performance, and thread utilization across MPI ranks.
+
+#### Usage
+
+```bash
+python pa_vis.py -d <output_directory>
+```
+
+**Command-line options:**
+- `-d, --directory`: Directory containing performance log files (default: current directory)
+- `--use-balance-logs`: Force use of `rank_*_balance.log` files
+- `--use-dat-files`: Force use of `*_report-rank*-step*.dat` files  
+- `--use-thread-files`: Force use of `thread_info_MPI-step*.dat` files
+
+The script automatically detects and processes the following data sources (in order of preference):
+1. **Balance logs**: `rank_memory_balance.log` and `rank_cpu_balance.log`
+2. **Individual report files**: `memuse_report-rank*-step*.dat` and `mpiuse_report-rank*-step*.dat`
+3. **Thread timing data**: `thread_info_MPI-step*.dat` or `thread_info-step*.dat`
+
+#### Output
+
+The tool generates a two-page PDF report:
+
+**Page 1: System-level Performance**
+- Memory usage across MPI ranks (average and peak)
+- CPU time distribution and parallel efficiency
+- Load balancing metrics
+
+**Page 2: Thread-level Analysis**
+- Individual thread utilization by rank
+- Thread balance ratios within each rank
+- Thread efficiency visualization
+
+#### Example Usage
+
+For a simulation run with directory `data-rivers-1-4-4-cosma`:
+
+```bash
+python pa_vis.py -d data-rivers-1-4-4-cosma
+```
+
+**Sample Output:**
+```
+Detected partition: cosma
+Searching for performance files in: .../SWIFT/examples/HumanMobility/data-rivers-1-4-4-cosma
+Detected 1 nodes from directory name
+Output will be saved as: pa_vis-1-4-4-cosma.pdf
+Using balance log files: .../rank_memory_balance.log, .../rank_cpu_balance.log
+Using MPI thread info files: 100 files found
+Found 4 ranks and 17 balance steps
+
+============================================================
+PERFORMANCE ANALYSIS SUMMARY
+============================================================
+Simulation: 17 steps, 4 MPI ranks
+Memory & CPU steps analyzed: 17, 25, 33, 41, 49, 57, 65, 75, 90, 126, 190, 252, 370, 524, 635, 745, 934
+Thread steps analyzed: 2 to 992 (100 total steps)
+
+MEMORY USAGE:
+  Rank 0: Avg = 1196.63 GB, Max = 1242.03 GB
+  Rank 1: Avg = 1167.23 GB, Max = 1201.75 GB
+  Rank 2: Avg = 1165.93 GB, Max = 1233.33 GB
+  Rank 3: Avg = 1151.01 GB, Max = 1208.53 GB
+  Overall average: 1170.20 GB
+  Memory balance: 1.4%
+
+CPU USAGE:
+  Rank 0: Total = 81.9 seconds
+  Rank 1: Total = 81.0 seconds
+  Rank 2: Total = 81.0 seconds
+  Rank 3: Total = 79.8 seconds
+  Overall total: 323.8 seconds
+  Parallel efficiency: 98.8%
+  Load balance ratio: 0.97
+
+INDIVIDUAL THREAD USAGE:
+  Rank 0-3 Thread 0-3: 97.7 - 246.8 s (per thread)
+  Total threads active: 16
+  Overall total thread time: 2114.4 s
+  Thread balance ratio: 0.40
+
+Output saved to: pa_vis-1-4-4-cosma.pdf
+============================================================
+```
+
+The output PDF filename automatically includes the run configuration (e.g., `pa_vis-1-4-4-cosma.pdf`) for easy identification and comparison across different configurations.
\ No newline at end of file
diff --git a/examples/HumanMobility/perf/paws.md b/examples/HumanMobility/doc/paws.md
similarity index 100%
rename from examples/HumanMobility/perf/paws.md
rename to examples/HumanMobility/doc/paws.md
diff --git a/examples/HumanMobility/pa_vis.py b/examples/HumanMobility/pa_vis.py
index 5eebdde61..0b2442ccc 100644
--- a/examples/HumanMobility/pa_vis.py
+++ b/examples/HumanMobility/pa_vis.py
@@ -20,10 +20,10 @@
 from matplotlib.backends.backend_pdf import PdfPages
 import argparse
 
-def parse_balance_logs():
+def parse_balance_logs(search_dir):
     """Parse rank_memory_balance.log and rank_cpu_balance.log files"""
-    memfile = "rank_memory_balance.log"
-    cpufile = "rank_cpu_balance.log"
+    memfile = os.path.join(search_dir, "rank_memory_balance.log")
+    cpufile = os.path.join(search_dir, "rank_cpu_balance.log")
     
     if not (os.path.exists(memfile) and os.path.exists(cpufile)):
         return None, None
@@ -45,15 +45,22 @@ def parse_balance_logs():
     
     return memdat, cpudat
 
-def parse_thread_info_files():
-    """Parse thread_info_MPI-step*.dat files for thread usage analysis"""
-    thread_files = glob.glob("thread_info_MPI-step*.dat")
+def parse_thread_info_files(search_dir):
+    """Parse thread_info files for both serial and MPI runs"""
+    # Try MPI files first, then serial files
+    thread_files = glob.glob(os.path.join(search_dir, "thread_info_MPI-step*.dat"))
+    is_mpi = True
     
     if not thread_files:
-        print("No thread_info_MPI files found")
+        # Try serial pattern
+        thread_files = glob.glob(os.path.join(search_dir, "thread_info-step*.dat"))
+        is_mpi = False
+    
+    if not thread_files:
+        print(f"No thread_info files found in {search_dir}")
         return None
     
-    print(f"Using thread info files: {len(thread_files)} files found")
+    print(f"Using {'MPI' if is_mpi else 'serial'} thread info files: {len(thread_files)} files found")
     
     # Parse thread timing data
     thread_data = {}  # rank -> {step -> {thread_id -> total_time}}
@@ -71,7 +78,7 @@ def parse_thread_info_files():
             if data.size == 0:
                 continue
                 
-            # First row contains metadata: rank, tic_step, toc_step, etc.
+            # Handle both single row and multi-row files
             if len(data.shape) == 1:
                 # Single row file
                 continue
@@ -86,16 +93,24 @@ def parse_thread_info_files():
             # Format: rank rid type subtype pair tic toc ci.hydro.count cj.hydro.count ci.grav.count cj.grav.count flags sid
             if len(task_data.shape) == 2:
                 for row in task_data:
-                    if len(row) >= 7:  # Ensure we have enough columns
-                        rank = int(row[0])  # First column is rank
-                        thread_id = int(row[1])  # rid = runner/thread ID
-                        tic = int(row[5])
-                        toc = int(row[6])
+                    if len(row) >= 7:
+                        if is_mpi:
+                            # MPI format: rank rid type subtype pair tic toc ...
+                            rank = int(row[0])
+                            thread_id = int(row[1])
+                            tic = int(row[5])
+                            toc = int(row[6])
+                        else:
+                            # Serial format: rid type subtype pair tic toc ...
+                            rank = 0  # Serial always uses rank 0
+                            thread_id = int(row[0])
+                            tic = int(row[4])
+                            toc = int(row[5])
                         
                         if toc > tic:  # Valid task timing
-                            task_time = toc - tic  # Time in ticks
+                            task_time = toc - tic
                             
-                            # Initialize rank data structure
+                            # Initialize data structures
                             if rank not in thread_data:
                                 thread_data[rank] = {}
                                 rank_thread_counts[rank] = 0
@@ -118,56 +133,63 @@ def parse_thread_info_files():
     
     return thread_data, rank_thread_counts
 
-def parse_dat_files():
-    """Parse memuse_report-rank*-step*.dat and mpiuse_report-rank*-step*.dat files"""
-    memfiles = glob.glob("memuse_report-rank*-step*.dat")
-    cpufiles = glob.glob("mpiuse_report-rank*-step*.dat")  # MPI communication files
+def parse_dat_files(search_dir):
+    """Parse memory and communication files for both serial and MPI runs"""
+    # Try MPI patterns first
+    memfiles = glob.glob(os.path.join(search_dir, "memuse_report-rank*-step*.dat"))
+    cpufiles = glob.glob(os.path.join(search_dir, "mpiuse_report-rank*-step*.dat"))
+    is_mpi = True
+    
+    if not memfiles:
+        # Try serial patterns
+        memfiles = glob.glob(os.path.join(search_dir, "memuse_report-step*.dat"))
+        cpufiles = []  # No MPI communication files in serial
+        is_mpi = False
     
     if not memfiles:
-        print("No memuse_report files found")
+        print(f"No memuse_report files found in {search_dir}")
         return None, None
     
-    print(f"Using .dat files: {len(memfiles)} memuse files, {len(cpufiles)} mpiuse files")
+    print(f"Using {'MPI' if is_mpi else 'serial'} .dat files: {len(memfiles)} memuse files, {len(cpufiles)} mpiuse files")
     
     # Parse memory usage files
     mem_data = []
     for memfile in sorted(memfiles):
-        # Extract rank and step from filename: memuse_report-rank0-step1.dat
-        parts = os.path.basename(memfile).replace('.dat', '').split('-')
-        rank = int(parts[1].replace('rank', ''))
-        step = int(parts[2].replace('step', ''))
+        if is_mpi:
+            # Extract rank and step from: memuse_report-rank0-step1.dat
+            parts = os.path.basename(memfile).replace('.dat', '').split('-')
+            rank = int(parts[1].replace('rank', ''))
+            step = int(parts[2].replace('step', ''))
+        else:
+            # Extract step from: memuse_report-step1.dat
+            parts = os.path.basename(memfile).replace('.dat', '').split('-')
+            rank = 0  # Serial always uses rank 0
+            step = int(parts[1].replace('step', ''))
         
         try:
-            # Load memory data - format varies, so we need to be careful
+            # Load memory data
             with open(memfile, 'r') as f:
                 lines = f.readlines()
                 
-            # Find peak memory usage from comments or data
+            # Find peak memory usage from comments
             peak_mem = 0
             for line in lines:
-                if line.startswith('# Peak memory usage'):
-                    # Extract MB value and convert to KB
-                    peak_mb = float(line.split(':')[1].strip().split()[0])
-                    peak_mem = peak_mb * 1024  # Convert MB to KB
-                    break
-            
-            if peak_mem == 0:
-                # Try to get from process memory line
-                for line in lines:
-                    if 'Memory use by process' in line:
-                        # Try to extract memory value - this is system dependent
-                        try:
-                            parts = line.split()
-                            for i, part in enumerate(parts):
-                                if 'MB' in part or 'KB' in part:
-                                    val = float(parts[i-1])
-                                    if 'MB' in part:
-                                        peak_mem = val * 1024
-                                    else:
-                                        peak_mem = val
-                                    break
-                        except:
-                            peak_mem = 1000000  # Default 1GB in KB
+                if line.startswith('# Peak memory usage') or 'Memory use by process' in line:
+                    try:
+                        # Extract memory value
+                        parts = line.split()
+                        for i, part in enumerate(parts):
+                            if 'MB' in part:
+                                val = float(parts[i-1])
+                                peak_mem = val * 1024  # Convert MB to KB
+                                break
+                            elif 'KB' in part:
+                                val = float(parts[i-1])
+                                peak_mem = val
+                                break
+                    except:
+                        pass
+                    if peak_mem > 0:
                         break
             
             if peak_mem == 0:
@@ -179,45 +201,48 @@ def parse_dat_files():
             print(f"Warning: Could not parse {memfile}: {e}")
             continue
     
-    # Parse MPI/CPU usage files (these represent communication time, not total CPU)
+    # For serial runs, create dummy CPU data since there's no MPI communication
     cpu_data = []
-    for cpufile in sorted(cpufiles):
-        # Extract rank and step from filename
-        parts = os.path.basename(cpufile).replace('.dat', '').split('-')
-        rank = int(parts[1].replace('rank', ''))
-        step = int(parts[2].replace('step', ''))
-        
-        try:
-            # Load MPI communication data
-            with open(cpufile, 'r') as f:
-                lines = f.readlines()
-            
-            # Sum up communication times as a proxy for CPU usage
-            total_comm_time = 0
-            for line in lines:
-                if line.startswith('#') or not line.strip():
-                    continue
-                try:
-                    # Format: stic etic dtic step rank otherrank type itype subtype isubtype activation tag size sum
-                    parts = line.split()
-                    if len(parts) >= 3:
-                        dtic = float(parts[2])  # Duration of MPI operation
-                        total_comm_time += dtic
-                except:
-                    continue
+    if not is_mpi and mem_data:
+        # Create minimal CPU data for serial runs
+        for step, rank, _ in mem_data:
+            cpu_data.append((step, rank, 0, 0, 1000, 0))  # Dummy values
+    elif is_mpi:
+        # Parse MPI communication files as before
+        for cpufile in sorted(cpufiles):
+            # Extract rank and step from filename
+            parts = os.path.basename(cpufile).replace('.dat', '').split('-')
+            rank = int(parts[1].replace('rank', ''))
+            step = int(parts[2].replace('step', ''))
             
-            # Convert ticks to milliseconds (approximate)
-            cpu_time_ms = total_comm_time / 1000.0  # Rough conversion
-            cpu_data.append((step, rank, 0, 0, cpu_time_ms, 0))  # user, sys, sum, deadfrac
+            try:
+                # Load MPI communication data
+                with open(cpufile, 'r') as f:
+                    lines = f.readlines()
+                
+                # Sum up communication times as a proxy for CPU usage
+                total_comm_time = 0
+                for line in lines:
+                    if line.startswith('#') or not line.strip():
+                        continue
+                    try:
+                        # Format: stic etic dtic step rank otherrank type itype subtype isubtype activation tag size sum
+                        parts = line.split()
+                        if len(parts) >= 3:
+                            dtic = float(parts[2])  # Duration of MPI operation
+                            total_comm_time += dtic
+                    except:
+                        continue
+                
+                # Convert ticks to milliseconds (approximate)
+                cpu_time_ms = total_comm_time / 1000.0  # Rough conversion
+                cpu_data.append((step, rank, 0, 0, cpu_time_ms, 0))  # user, sys, sum, deadfrac
             
-        except Exception as e:
-            print(f"Warning: Could not parse {cpufile}: {e}")
-            continue
-    
-    if not mem_data and not cpu_data:
-        return None, None
+            except Exception as e:
+                print(f"Warning: Could not parse {cpufile}: {e}")
+                continue
     
-    # Convert to numpy arrays with same format as balance logs
+    # Convert to numpy arrays
     if mem_data:
         memdat = np.array(mem_data, dtype=[("step", int), ("rank", int), ("resident", float)])
     else:
@@ -241,6 +266,55 @@ def get_node_assignments(num_ranks):
         mid_point = num_ranks // 2
         return ["background"] * mid_point + ["zoom"] * (num_ranks - mid_point)
 
+def extract_run_info_from_directory(search_dir):
+    """Extract run information from directory name for PDF naming"""
+    import re
+    
+    dir_name = os.path.basename(search_dir)
+    
+    # Try to parse patterns like "data-rivers-N-R-C" where N=nodes, R=ranks, C=cores
+    patterns = [
+        r'data-.*-(\d+)-(\d+)-(\d+)(?:-cosma)?$',  # data-rivers-1-2-8-cosma or data-rivers-1-2-8
+        r'.*-(\d+)nodes?-(\d+)ranks?-(\d+)cores?',  # flexible naming
+        r'.*-N(\d+)-R(\d+)-C(\d+)',  # explicit N-R-C format
+        r'.*-(\d+)-(\d+)-(\d+)$',  # generic three numbers
+    ]
+    
+    for pattern in patterns:
+        match = re.search(pattern, dir_name)
+        if match:
+            nodes, ranks, cores = map(int, match.groups())
+            # Check if directory name contains "cosma" (legacy partition indicator)
+            if 'cosma' in dir_name and not 'cosma5' in dir_name:
+                return f"{nodes}-{ranks}-{cores}-cosma", nodes  # Return both filename and node count
+            else:
+                return f"{nodes}-{ranks}-{cores}", nodes
+    
+    # Fallback: use directory name if no pattern matches
+    return dir_name, None
+
+def detect_partition_from_directory(search_dir):
+    """Detect SLURM partition from directory name"""
+    dir_name = os.path.basename(search_dir)
+    
+    # Check directory name for partition indicators
+    if 'cosma' in dir_name.lower() and 'cosma5' not in dir_name.lower():
+        return 'cosma'
+    else:
+        # Default to cosma5 if no "cosma" suffix or if it's a different pattern
+        return 'cosma5'
+
+def format_title_with_partition(base_title, num_steps, num_ranks, num_nodes, partition):
+    """Format title with partition information"""
+    if num_nodes is not None:
+        middle_part = f"{num_steps} selected steps across {num_ranks} MPI ranks on {num_nodes} nodes"
+    else:
+        middle_part = f"{num_steps} selected steps across {num_ranks} MPI ranks"
+    
+    partition_suffix = f" on {partition}"
+    
+    return f"{base_title}\n{middle_part}{partition_suffix}"
+
 def main():
     parser = argparse.ArgumentParser(description='Performance Analysis Visualization')
     parser.add_argument('--use-balance-logs', action='store_true', 
@@ -249,8 +323,29 @@ def main():
                        help='Force use of *_report-rank*-step*.dat files')
     parser.add_argument('--use-thread-files', action='store_true',
                        help='Force use of thread_info_MPI-step*.dat files')
+    parser.add_argument('--directory', '-d', type=str, default='.',
+                       help='Directory to search for log and dat files (default: current directory)')
     args = parser.parse_args()
     
+    # Set the working directory for file searches
+    search_dir = os.path.abspath(args.directory)
+    if not os.path.exists(search_dir):
+        print(f"Error: Directory '{search_dir}' does not exist!")
+        sys.exit(1)
+    
+    # Detect partition from directory name
+    partition = detect_partition_from_directory(search_dir)
+    print(f"Detected partition: {partition}")
+    
+    # Extract run info and node count for PDF naming
+    run_info, num_nodes = extract_run_info_from_directory(search_dir)
+    pdf_filename = f"pa_vis-{run_info}.pdf"
+    
+    print(f"Searching for performance files in: {search_dir}")
+    if num_nodes is not None:
+        print(f"Detected {num_nodes} nodes from directory name")
+    print(f"Output will be saved as: {pdf_filename}")
+    
     # Color mapping
     cmap = {"zoom": "tab:red", "background": "tab:blue", "compute": "tab:green"}
     
@@ -259,25 +354,25 @@ def main():
     thread_data, rank_thread_counts = None, None
     
     if args.use_thread_files:
-        thread_result = parse_thread_info_files()
+        thread_result = parse_thread_info_files(search_dir)
         if thread_result:
             thread_data, rank_thread_counts = thread_result
     elif args.use_dat_files:
-        memdat, cpudat = parse_dat_files()
-        thread_result = parse_thread_info_files()
+        memdat, cpudat = parse_dat_files(search_dir)
+        thread_result = parse_thread_info_files(search_dir)
         if thread_result:
             thread_data, rank_thread_counts = thread_result
     elif args.use_balance_logs:
-        memdat, cpudat = parse_balance_logs()
+        memdat, cpudat = parse_balance_logs(search_dir)
     else:
         # Try balance logs first, then .dat files, then thread files
-        memdat, cpudat = parse_balance_logs()
+        memdat, cpudat = parse_balance_logs(search_dir)
         if memdat is None or cpudat is None:
             print("Balance logs not found, trying .dat files...")
-            memdat, cpudat = parse_dat_files()
+            memdat, cpudat = parse_dat_files(search_dir)
         
         # Always try to get thread data
-        thread_result = parse_thread_info_files()
+        thread_result = parse_thread_info_files(search_dir)
         if thread_result:
             thread_data, rank_thread_counts = thread_result
     
@@ -288,6 +383,7 @@ def main():
         print("  - memuse_report-rank*-step*.dat files")
         print("  - mpiuse_report-rank*-step*.dat files")
         print("  - thread_info_MPI-step*.dat files")
+        print("  - thread_info_step*.dat files")
         sys.exit(1)
     
     # Get unique ranks and steps
@@ -324,6 +420,17 @@ def main():
     print(f"Found {num_ranks} ranks and {num_balance_steps} balance steps")
     print(f"Ranks: {unique_ranks}")
     print(f"Balance Steps: {balance_steps}")
+
+    # Add information about thread steps
+    if num_thread_steps > 0:
+        print(f"Thread Steps: {num_thread_steps} steps found")
+        if num_thread_steps <= 10:
+            print(f"  Steps: {thread_steps}")
+        else:
+            print(f"  First 5: {thread_steps[:5]}")
+            print(f"  Last 5: {thread_steps[-5:]}")
+    else:
+        print("Thread Steps: None")
     
     # Set up node assignments
     node_assignments = get_node_assignments(num_ranks)
@@ -395,11 +502,14 @@ def main():
     # Create the two-page visualization
     print("Creating performance analysis visualization...")
     
-    with PdfPages("pa_vis.pdf") as pdffile:
+    with PdfPages(pdf_filename) as pdffile:
         
         # PAGE 1: Memory Usage and CPU Usage
         has_memory_data = memdat is not None and np.any(accumulated_memory > 0)
         has_cpu_data = cpudat is not None and np.any(accumulated_cpu > 0)
+        
+        # Initialize plot_idx before the conditional block to avoid UnboundLocalError
+        plot_idx = 0
 
         if has_memory_data or has_cpu_data:
             # Calculate number of plots needed
@@ -408,11 +518,16 @@ def main():
             # Fix: Increase figure width to accommodate both charts properly
             fig1, axs1 = plt.subplots(1, 2, figsize=(16, 6))
             
-            # Fix: Center the title properly by adjusting spacing and using figure-level suptitle
-            fig1.suptitle(f"Memory and CPU Performance Analysis\n{num_balance_steps} selected steps across {num_ranks} MPI ranks", 
-                         fontsize=14, ha='center', va='top', y=0.95)
+            # Update title formatting with partition information
+            title1 = format_title_with_partition(
+                "Memory and CPU Performance Analysis", 
+                num_balance_steps, 
+                num_ranks, 
+                num_nodes,
+                partition
+            )
             
-            plot_idx = 0
+            fig1.suptitle(title1, fontsize=14, ha='center', va='top', y=0.95)
             
             # Memory usage chart
             if has_memory_data:
@@ -471,14 +586,23 @@ def main():
                 axs1[plot_idx].set_xticks(unique_ranks)
                 plot_idx += 1
         
-        # Hide unused subplot if only one plot
-        if plot_idx == 1:
-            axs1[1].set_visible(False)
-        
-        # Fix: Adjust spacing for wider figure and better layout
-        plt.subplots_adjust(top=0.85, bottom=0.15, left=0.08, right=0.95, wspace=0.25)
-        plt.savefig(pdffile, format="pdf", bbox_inches='tight', dpi=150)
-        plt.close()
+            # Hide unused subplot if only one plot
+            if plot_idx == 1:
+                axs1[1].set_visible(False)
+            
+            # Fix: Adjust spacing for wider figure and better layout
+            plt.subplots_adjust(top=0.85, bottom=0.15, left=0.08, right=0.95, wspace=0.25)
+            plt.savefig(pdffile, format="pdf", bbox_inches='tight', dpi=150)
+            plt.close()
+
+        elif thread_data is not None and individual_thread_data:
+            # If no memory or CPU data but thread data exists, create a message for page 1
+            fig1 = plt.figure(figsize=(10, 6))
+            plt.text(0.5, 0.5, "No Memory or CPU usage data available.\nSee next page for Thread Usage data.", 
+                    ha='center', va='center', fontsize=16)
+            plt.axis('off')
+            plt.savefig(pdffile, format="pdf", bbox_inches='tight', dpi=150)
+            plt.close()
         
         # PAGE 2: Individual Thread Usage (separated by rank)
         if thread_data is not None and individual_thread_data:
@@ -503,8 +627,16 @@ def main():
                 else:
                     axs2 = [axs2] if ncols == 1 else axs2
             
-            fig2.suptitle(f"Individual Thread Usage by Rank\n{num_thread_steps} selected steps across {num_ranks} MPI ranks", 
-                         fontsize=14)
+            # For Page 2 title  
+            title2 = format_title_with_partition(
+                "Individual Thread Usage by Rank", 
+                num_thread_steps, 
+                num_ranks, 
+                num_nodes,
+                partition
+            )
+
+            fig2.suptitle(title2, fontsize=14)
             
             # Plot thread usage for each rank separately
             rank_idx = 0
@@ -672,7 +804,7 @@ def main():
         print("INDIVIDUAL THREAD USAGE: No data available")
     
     print()
-    print(f"Output saved to: pa_vis.pdf")
+    print(f"Output saved to: {pdf_filename}")
     print("="*60)
 
 if __name__ == "__main__":

From dec0fd10a93e8e589381a7a77bf929221a4aad61 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Wed, 18 Jun 2025 19:24:10 +0100
Subject: [PATCH 41/43] Improve job scheduling suite and extend documentation

---
 examples/HumanMobility/doc/job_scheduling.md |  95 +++++++++----
 examples/HumanMobility/gen.sh                |  69 +++++++--
 examples/HumanMobility/job.sh                |  46 +++++-
 examples/HumanMobility/makeRivers.py         |   8 +-
 examples/HumanMobility/map.sh                | 101 ++++++++++++-
 examples/HumanMobility/run.sh                |  66 +++++++--
 examples/HumanMobility/submit.sh             |  68 +++++++--
 examples/HumanMobility/vis.sh                | 142 +++++++++++++++++++
 examples/HumanMobility/visualise.sh          |  32 -----
 9 files changed, 518 insertions(+), 109 deletions(-)
 create mode 100755 examples/HumanMobility/vis.sh
 delete mode 100755 examples/HumanMobility/visualise.sh

diff --git a/examples/HumanMobility/doc/job_scheduling.md b/examples/HumanMobility/doc/job_scheduling.md
index 818b861c4..35b7e12ff 100644
--- a/examples/HumanMobility/doc/job_scheduling.md
+++ b/examples/HumanMobility/doc/job_scheduling.md
@@ -107,15 +107,16 @@ For strong scaling analysis, we keep the problem size constant (100 x 100 humans
 ```bash
 # Test MPI scaling across multiple nodes with fixed threads per rank
 ./submit.sh --run -N 2 -n 2 -c 128   # 2 nodes, 2 MPI ranks, 128 threads each
-./submit.sh --run -N 3 -n 3 -c 85    # 3 nodes, 3 MPI ranks, ~85 threads each
+./submit.sh --run -N 3 -n 3 -c 85    # 3 nodes, 3 MPI ranks, ~85 threads each (max COSMA5)
 ```
 
 **COSMA queue comparison:**
 ```bash
-# Compare with older COSMA hardware
+# Compare with older COSMA hardware (can use many more nodes)
 ./submit.sh --run -N 1 -n 1 -c 16 -p cosma   # 1 old COSMA node, 1 MPI rank, 16 threads (max)
 ./submit.sh --run -N 2 -n 2 -c 8 -p cosma    # 2 old COSMA nodes, 2 MPI ranks, 8 threads each
 ./submit.sh --run -N 4 -n 4 -c 4 -p cosma    # 4 old COSMA nodes, 4 MPI ranks, 4 threads each
+./submit.sh --run -N 8 -n 8 -c 2 -p cosma    # 8 old COSMA nodes, 8 MPI ranks, 2 threads each
 ```
 
 **Performance Analysis Integration:**
@@ -124,6 +125,7 @@ For strong scaling analysis, we keep the problem size constant (100 x 100 humans
 ./submit.sh --likwid -N 1 -n 1 -c 64    # LIKWID analysis on COSMA5
 ./submit.sh --maqao -N 1 -n 1 -c 16 -p cosma  # MAQAO analysis on COSMA
 ./submit.sh --vtune -N 2 -n 2 -c 128   # VTune analysis across 2 COSMA5 nodes
+./submit.sh --vtune -N 3 -n 3 -c 85    # VTune analysis across 3 COSMA5 nodes (maximum)
 ```
 
 #### Weak scaling
@@ -134,56 +136,93 @@ For weak scaling, we maintain constant work per computational unit by increasing
 
 **Scaling Strategy:**
 - When we double the number of nodes, we should increase the linear dimension by ~1.414 (√2)
-- For example: 100×100 humans → 141×141 humans → 200×200 humans (approximately)
+- For example: 100×100 humans → 141×141 humans → 173×173 humans (approximately)
+- **Important**: Each scaling configuration requires its own input files with appropriately sized domains and human populations
+
+**Step 1: Generate Input Files for Each Configuration**
+
+Before running weak scaling tests, generate the appropriate input files for each node count:
 
-**Baseline (2 nodes, 100 x 100 humans on 10 x 10 km):**
 ```bash
-./submit.sh --run -N 2 -n 2 -c 16 -p cosma  # 2 nodes, 2 ranks (1 per node), 16 threads each
-# or on COSMA5:
-./submit.sh --run -N 2 -n 2 -c 128          # 2 nodes, 2 ranks, 128 threads each
+# For 2 nodes baseline (100x100 humans on 10x10 km)
+./submit.sh --gen -- -n 100 -b 10000
+# Creates: humans-rivers-100.hdf5, river-rivers-100.hdf5 (or with -cosma suffix)
+
+# For 3 nodes (122x122 humans on 12.2x12.2 km) - COSMA5 maximum  
+./submit.sh --gen -- -n 122 -b 12200
+# Creates: humans-rivers-122.hdf5, river-rivers-122.hdf5
+
+# For extended scaling on COSMA legacy hardware
+# For 8 nodes (200x200 humans on 20x20 km)
+./submit.sh --gen -- -n 200 -b 20000
+# Creates: humans-rivers-200.hdf5, river-rivers-200.hdf5
+
+# For 18 nodes (300x300 humans on 30x30 km)
+./submit.sh --gen -- -n 300 -b 30000
+# Creates: humans-rivers-300.hdf5, river-rivers-300.hdf5
 ```
 
-**Scale to 8 nodes (200 x 200 humans on 20 x 20 km):**
+**Step 2: Run Weak Scaling Tests**
+
+Now run the simulations using the corresponding input files for each configuration:
+
+**Baseline (2 nodes, 100 x 100 humans on 10 x 10 km):**
 ```bash
-./submit.sh --run -N 8 -n 8 -c 16 -p cosma  # 8 nodes, 8 ranks (1 per node), 16 threads each
+./submit.sh --run -N 2 -n 2 -c 16 -p cosma -- --num-humans 100  # Uses humans-rivers-100.hdf5
 # or on COSMA5:
-./submit.sh --run -N 8 -n 8 -c 128          # 8 nodes, 8 ranks, 128 threads each
+./submit.sh --run -N 2 -n 2 -c 128 -- --num-humans 100         # Uses humans-rivers-100.hdf5
 ```
 
-**Scale to 18 nodes (300 x 300 humans on 30 x 30 km):**
+**Scale to 3 nodes (122 x 122 humans on 12.2 x 12.2 km) - COSMA5 maximum:**
 ```bash
-./submit.sh --run -N 18 -n 18 -c 16 -p cosma  # 18 nodes, 18 ranks (1 per node), 16 threads each
+./submit.sh --run -N 3 -n 3 -c 16 -p cosma -- --num-humans 122  # Uses humans-rivers-122.hdf5
 # or on COSMA5:
-./submit.sh --run -N 18 -n 18 -c 128          # 18 nodes, 18 ranks, 128 threads each
+./submit.sh --run -N 3 -n 3 -c 64 -- --num-humans 122          # Uses humans-rivers-122.hdf5
 ```
 
-**Important**: When submitting these jobs, you'll need to adjust your initial conditions file to match the problem size. Use the `makeIC.py` script with the appropriate `-n` parameter:
-
+**Extended scaling on COSMA (legacy hardware with more nodes available):**
 ```bash
-# For 2 nodes (100x100 humans)
-python makeIC.py -n 100 -o humans-rivers-3.hdf5
+./submit.sh --run -N 8 -n 8 -c 16 -p cosma -- --num-humans 200   # Uses humans-rivers-200.hdf5
+./submit.sh --run -N 18 -n 18 -c 16 -p cosma -- --num-humans 300 # Uses humans-rivers-300.hdf5
+```
 
-# For 8 nodes (200x200 humans)
-python makeIC.py -n 200 -o humans-rivers-3.hdf5
+**Step 3: Profile at Scale**
 
-# For 18 nodes (300x300 humans)
-python makeIC.py -n 300 -o humans-rivers-3.hdf5
-```
+Run performance analysis with the appropriate input files:
 
-**Profiling at Scale:**
 ```bash
 # Profile weak scaling performance at different node counts
-./submit.sh --likwid -N 2 -n 2 -c 16 -p cosma  # Baseline profiling (2 nodes)
-./submit.sh --likwid -N 8 -n 8 -c 16 -p cosma  # 8-node scaling analysis
-./submit.sh --vtune -N 18 -n 18 -c 16 -p cosma # 18-node analysis
+./submit.sh --likwid -N 2 -n 2 -c 64 -- --num-humans 100        # Baseline profiling (2 COSMA5 nodes)
+./submit.sh --likwid -N 3 -n 3 -c 64 -- --num-humans 122         # 3-node scaling analysis (max COSMA5)
+./submit.sh --vtune -N 8 -n 8 -c 16 -p cosma -- --num-humans 200 # 8-node analysis on COSMA legacy
+./submit.sh --vtune -N 18 -n 18 -c 16 -p cosma -- --num-humans 300 # 18-node analysis on COSMA legacy
 ```
 
+**Input File Naming Convention:**
+
+The [`gen.sh`](examples/HumanMobility/gen.sh) script generates files with names based on the `--num-humans` parameter:
+- `humans-rivers-{NUM_HUMANS}.hdf5` (or `humans-rivers-{NUM_HUMANS}-cosma.hdf5` on COSMA partition)
+- `river-rivers-{NUM_HUMANS}.hdf5` (or `river-rivers-{NUM_HUMANS}-cosma.hdf5` on COSMA partition)
+
+The [`run.sh`](examples/HumanMobility/run.sh) script automatically uses the correct input files when passed the `--num-humans` parameter, ensuring that:
+- 2-node runs use the 100×100 human configuration
+- 3-node runs use the 122×122 human configuration  
+- 8-node runs use the 200×200 human configuration
+- 18-node runs use the 300×300 human configuration
+
 **Expected Results:**
 - Execution time should remain approximately constant across all configurations
-- Memory usage per node should remain consistent
+- Memory usage per node should remain consistent (proportional to local problem size)
 - Communication overhead should grow modestly with node count
+- Each configuration maintains the same work per computational unit
+
+**Key Points for Weak Scaling:**
+1. **Generate all input files first** before running scaling tests
+2. **Use `--num-humans` parameter** to specify which input files to use
+3. **Ensure domain size scales with human count** (maintain constant density)
+4. **Verify file naming consistency** across partitions (COSMA vs COSMA5)
 
-_Note: For the most accurate weak scaling measurements, create separate initial condition files for each problem size and ensure the work per node remains constant across all configurations._
+_Note: COSMA5 is limited to 3 nodes maximum, so extended weak scaling studies should use the legacy COSMA partition which has ~160 nodes available. The input file generation step ensures that each node configuration has appropriately sized problems while maintaining constant work density._
 
 ### Visualisation of performance results
 
diff --git a/examples/HumanMobility/gen.sh b/examples/HumanMobility/gen.sh
index e6682d73c..9ba945a8d 100755
--- a/examples/HumanMobility/gen.sh
+++ b/examples/HumanMobility/gen.sh
@@ -1,27 +1,78 @@
 #!/bin/bash
 
+# Default parameters
+BOX_SIZE=100000
+GRID_SIZE=10000
+NUM_HUMANS=1000
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        -b|--box-size)
+            BOX_SIZE="$2"
+            shift 2
+            ;;
+        -g|--grid-size)
+            GRID_SIZE="$2"
+            shift 2
+            ;;
+        -n|--num-humans)
+            NUM_HUMANS="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  -b, --box-size BOX_SIZE     Box size in meters (default: 100000)"
+            echo "  -g, --grid-size GRID_SIZE   Grid size for acceleration field (default: 10000)"
+            echo "  -n, --num-humans NUM_HUMANS Number of humans per dimension (default: 1000)"
+            echo "  -h, --help                  Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use -h or --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Determine partition suffix based on SLURM partition
+partition_suffix=""
+if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+    partition_suffix="-cosma"
+fi
+
 # Basenames for this run (without extensions)
-HUMANS=humans-rivers-3
-RIVERS=river-rivers-3
-DATA=data-rivers-3
-IMAGES=images-rivers-3
+HUMANS=humans-rivers-${NUM_HUMANS}${partition_suffix}
+RIVERS=river-rivers-${NUM_HUMANS}${partition_suffix}
+
+echo "Generation Configuration:"
+echo "  Box size: ${BOX_SIZE} m"
+echo "  Grid size: ${GRID_SIZE} cells"
+echo "  Number of humans: ${NUM_HUMANS}x${NUM_HUMANS}"
+echo "  Partition: ${SLURM_JOB_PARTITION:-cosma5}"
+echo "  Output files: ${HUMANS}.hdf5, ${RIVERS}.hdf5"
+echo ""
 
 # Remove previously generated data and images to regenerate the new ones
 rm -f ${HUMANS}.hdf5
 rm -f ${RIVERS}.hdf5
-rm -f ${DATA}/*
-rm -f ${IMAGES}/*
 
 # Generate acceleration field for river
 if [ ! -e ${RIVERS}.hdf5 ]
 then
     echo "Generating acceleration field for the river..."
-    python3 makeRivers.py -b 100000 -g 10000 -f ${RIVERS}.hdf5
+    python3 makeRivers.py -b ${BOX_SIZE} -g ${GRID_SIZE} -f ${RIVERS}.hdf5
 fi
 
 # Generate the initial conditions if they are not present.
 if [ ! -e ${HUMANS}.hdf5 ]
 then
     echo "Generating initial conditions for the human mobility box example..."
-    python3 makeIC.py -t gas -n 1000 -b 100000 -f ${HUMANS}.hdf5  # -t particles
-fi
\ No newline at end of file
+    python3 makeIC.py -t gas -n ${NUM_HUMANS} -b ${BOX_SIZE} -f ${HUMANS}.hdf5
+fi
+
+echo "Generation complete!"
+echo "  River file: ${RIVERS}.hdf5"
+echo "  Humans file: ${HUMANS}.hdf5"
\ No newline at end of file
diff --git a/examples/HumanMobility/job.sh b/examples/HumanMobility/job.sh
index 7a44f1a50..be19bb28e 100644
--- a/examples/HumanMobility/job.sh
+++ b/examples/HumanMobility/job.sh
@@ -6,12 +6,13 @@
 # Note: SLURM partition and other parameters are passed from submit.sh
 
 # Check if an argument was provided
-if [ $# -ne 1 ]; then
-    echo "Usage: $0 [--gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep]"
+if [ $# -lt 1 ]; then
+    echo "Usage: $0 [--gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep] [mode_specific_params]"
     exit 1
 fi
 
 mode=$1
+shift  # Remove mode from arguments
 
 case $mode in
     --gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
@@ -22,6 +23,9 @@ case $mode in
         ;;
 esac
 
+# Store remaining arguments as mode-specific parameters
+mode_params="$*"
+
 # Detect partition and set hardware description
 if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
     hardware_desc="COSMA (legacy hardware)"
@@ -40,11 +44,35 @@ echo "  MPI Tasks: ${SLURM_NTASKS:-1}"
 echo "  CPUs per Task: ${SLURM_CPUS_PER_TASK:-16}"
 echo "  Memory per Node: ${SLURM_MEM_PER_NODE:-64G}"
 
+# Show mode-specific parameters if provided
+if [ -n "$mode_params" ]; then
+    case $mode in
+        --gen)
+            echo "  Generation Parameters: $mode_params"
+            ;;
+        --vis)
+            echo "  Visualization Parameters: $mode_params"
+            ;;
+        --map)
+            echo "  Map Parameters: $mode_params"
+            ;;
+        --run)
+            echo "  Run Parameters: $mode_params"
+            ;;
+        --likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
+            echo "  Performance Analysis Parameters: $mode_params"
+            ;;
+    esac
+fi
+
 # Common module loading
 module purge
 module load cosma
 module load $intel_comp_version
-module load umf compiler-rt tbb compiler mpi
+if [[ "$intel_comp_version" == "intel_comp/2025.0.1" ]]; then
+    module load umf
+fi
+module load compiler-rt tbb compiler mpi
 # module load openmpi/5.0.3/
 
 # Set MPI environment variables
@@ -61,17 +89,21 @@ module load sundials/5.8.0_c8_single
 # Load analysis-specific modules and execute the appropriate script
 case $mode in
     --gen)
-        ./gen.sh
+        echo "Running generation with parameters: $mode_params"
+        ./gen.sh $mode_params
         ;;
     --vis)
         module load ffmpeg
-        ./visualise.sh
+        echo "Running visualization with parameters: $mode_params"
+        ./vis.sh $mode_params
         ;;
     --map)
-        ./map.sh
+        echo "Running map generation with parameters: $mode_params"
+        ./map.sh $mode_params
         ;;
     --run)
-        ./run.sh
+        echo "Running simulation with parameters: $mode_params"
+        ./run.sh $mode_params
         ;;
     --likwid)
         # Load likwid profiler
diff --git a/examples/HumanMobility/makeRivers.py b/examples/HumanMobility/makeRivers.py
index eb942a0a6..ae86ee9e9 100644
--- a/examples/HumanMobility/makeRivers.py
+++ b/examples/HumanMobility/makeRivers.py
@@ -5,6 +5,7 @@
 from scipy.interpolate import splprep, splev
 import concurrent.futures
 import time
+import os
 
 def generate_meandering_river(box_size, num_control_points=8, river_width=60.0, randomness=0.15):
     """Generate a meandering river using control points and spline interpolation"""
@@ -260,13 +261,18 @@ def main():
     print("Calculating acceleration field for bottom-left block...")
     accel_start_time = time.time()
 
-    with concurrent.futures.ProcessPoolExecutor() as executor:
+    # Limit the number of worker processes to avoid "too many open files" error
+    # Use at most 16 processes or the number of CPU cores, whichever is smaller
+    max_workers = min(16, os.cpu_count() or 1)
+    
+    with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
         futures = []
         for i, x in enumerate(x_block):
             if(i % 100 == 0):
                 print(f"Processing row {i+1}/{block_grid_size[0]}")
             # Submit tasks to the executor
             futures.append(executor.submit(compute_row_block, i, x, y_block, river_segments, mass, distance))
+        
         for future in concurrent.futures.as_completed(futures):
             i, ax_row, ay_row = future.result()
             ax_block[i, :] = ax_row
diff --git a/examples/HumanMobility/map.sh b/examples/HumanMobility/map.sh
index c5bf31fde..9956e59c9 100755
--- a/examples/HumanMobility/map.sh
+++ b/examples/HumanMobility/map.sh
@@ -1,15 +1,102 @@
 #!/bin/bash
 
-# Remove previously generated river images (do not remove the directory itself)
-rm images-rivers-3/rivers.png 2>/dev/null
-
-# Set parameters
+# Default parameters
+NUM_HUMANS=1000
 min_x=0
 max_x=100000
 min_y=0
 max_y=100000
-river_base="river-rivers-3"
-output_image_base="images-rivers-3/rivers"
+
+# Parse command line arguments
+dir_suffix=""
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --num-humans)
+            NUM_HUMANS="$2"
+            shift 2
+            ;;
+        -d|--dir-suffix)
+            dir_suffix="$2"
+            shift 2
+            ;;
+        --min-x)
+            min_x="$2"
+            shift 2
+            ;;
+        --max-x)
+            max_x="$2"
+            shift 2
+            ;;
+        --min-y)
+            min_y="$2"
+            shift 2
+            ;;
+        --max-y)
+            max_y="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --num-humans NUM_HUMANS     Number of humans per dimension (default: 1000)"
+            echo "  -d, --dir-suffix <suffix>   Suffix part of directory path to read data from"
+            echo "  --min-x <value>             Minimum x coordinate for map bounds (default: 0)"
+            echo "  --max-x <value>             Maximum x coordinate for map bounds (default: 100000)"
+            echo "  --min-y <value>             Minimum y coordinate for map bounds (default: 0)"
+            echo "  --max-y <value>             Maximum y coordinate for map bounds (default: 100000)"
+            echo "  -h, --help                  Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use -h or --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# If no dir_suffix provided, auto-generate from SLURM parameters
+if [ -z "$dir_suffix" ]; then
+    nodes=${SLURM_JOB_NUM_NODES:-1}
+    ntasks=${SLURM_NTASKS:-1}
+    cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+    
+    # Determine partition suffix
+    partition_suffix=""
+    if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+        partition_suffix="-cosma"
+    fi
+    
+    dir_suffix="${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}"
+else
+    # Need to determine partition suffix for filename generation
+    partition_suffix=""
+    if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]] || [[ "$dir_suffix" == *"-cosma" ]]; then
+        partition_suffix="-cosma"
+    fi
+fi
+
+# Basenames for this run (without extensions)
+RIVERS=river-rivers-${NUM_HUMANS}${partition_suffix}
+IMAGES=images-rivers-${dir_suffix}
+
+echo "Map Generation Configuration:"
+echo "  Directory suffix: ${dir_suffix}"
+echo "  Images directory: ${IMAGES}"
+echo "  Bounds: x=[${min_x}, ${max_x}], y=[${min_y}, ${max_y}]"
+
+# Create images directory if it doesn't exist
+mkdir -p ${IMAGES}
+
+# Remove previously generated river images
+rm -f ${IMAGES}/rivers.png 2>/dev/null
+
+echo "Generating river map..."
+echo "  River file: ${RIVERS}.hdf5"
+echo "  Output image: ${IMAGES}/rivers.png"
+echo "  Bounds: x[${min_x}, ${max_x}], y[${min_y}, ${max_y}]"
 
 # Plot only the rivers
-python3 plot_rivers.py ${river_base}.hdf5 ${output_image_base}.png ${min_x} ${max_x} ${min_y} ${max_y}
\ No newline at end of file
+python3 plot_rivers.py ${RIVERS}.hdf5 ${IMAGES}/rivers.png ${min_x} ${max_x} ${min_y} ${max_y}
+
+echo "Map generation complete! Image saved to: ${IMAGES}/rivers.png"
\ No newline at end of file
diff --git a/examples/HumanMobility/run.sh b/examples/HumanMobility/run.sh
index 4f58be406..d25579445 100755
--- a/examples/HumanMobility/run.sh
+++ b/examples/HumanMobility/run.sh
@@ -1,14 +1,32 @@
 #!/bin/bash
 
-# Basenames for this run (without extensions)
-HUMANS=humans-rivers-3
-RIVERS=river-rivers-3
-HUMANMOBILITY=humanMobility
+# Default parameters
+NUM_HUMANS=1000
 
-# Automatically generate directory names based on SLURM parameters
-nodes=${SLURM_JOB_NUM_NODES:-1}
-ntasks=${SLURM_NTASKS:-1}
-cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --num-humans)
+            NUM_HUMANS="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --num-humans NUM_HUMANS Number of humans per dimension (default: 1000)"
+            echo "  -h, --help              Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use -h or --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+echo "Run Configuration:"
+echo "  Number of humans: ${NUM_HUMANS}x${NUM_HUMANS}"
 
 # Determine partition suffix based on SLURM partition
 partition_suffix=""
@@ -16,13 +34,41 @@ if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
     partition_suffix="-cosma"
 fi
 
+# Basenames for this run (without extensions)
+HUMANS=humans-rivers-${NUM_HUMANS}${partition_suffix}
+RIVERS=river-rivers-${NUM_HUMANS}${partition_suffix}
+HUMANMOBILITY=humanMobility
+
+# Automatically generate directory names based on SLURM parameters
+nodes=${SLURM_JOB_NUM_NODES:-1}
+ntasks=${SLURM_NTASKS:-1}
+cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+
 # Generate suffix based on resources and partition
 DATA=data-rivers-${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}
 IMAGES=images-rivers-${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}
 
 # Create the data directory if it doesn't exist
+if [ -d "${DATA}" ]; then
+    rm -rf ${DATA}/*
+fi
 mkdir -p ${DATA}
 
+# Check if required input files exist
+if [ ! -f "${HUMANS}.hdf5" ]; then
+    echo "ERROR: Input file ${HUMANS}.hdf5 not found!"
+    echo "Please generate input files first with:"
+    echo "  ./submit.sh --gen -- -n ${NUM_HUMANS}"
+    exit 1
+fi
+
+if [ ! -f "${RIVERS}.hdf5" ]; then
+    echo "ERROR: Input file ${RIVERS}.hdf5 not found!"
+    echo "Please generate input files first with:"
+    echo "  ./submit.sh --gen -- -n ${NUM_HUMANS}"
+    exit 1
+fi
+
 # Render the YAML config from template
 export DATA HUMANMOBILITY HUMANS RIVERS
 envsubst < humanMobility_template.yml > ${DATA}/${HUMANMOBILITY}.yml
@@ -58,7 +104,7 @@ if [ $ntasks -eq 1 ]; then
     
     ${SWIFT} --threads=$cpus_per_task \
         --task-dumps=10 -v 1 \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+        -A -s -g -G --hm-river --hm-randomwalk -n ${NUM_HUMANS} ${HUMANMOBILITY}.yml
 else
     echo "Running PARALLEL version ($ntasks MPI ranks)"
     export OMP_NUM_THREADS=$cpus_per_task
@@ -66,7 +112,7 @@ else
     mpirun -n $ntasks \
         ${SWIFT_MPI} --threads=$cpus_per_task \
         --task-dumps=10 -v 1 \
-        -A -s -g -G --hm-river --hm-randomwalk -n 1000 ${HUMANMOBILITY}.yml
+        -A -s -g -G --hm-river --hm-randomwalk -n ${NUM_HUMANS} ${HUMANMOBILITY}.yml
 fi
 
 echo "Simulation complete. Check output files and SWIFT task logs for analysis in ${DATA}/"
\ No newline at end of file
diff --git a/examples/HumanMobility/submit.sh b/examples/HumanMobility/submit.sh
index cc8a60e56..7acea50da 100755
--- a/examples/HumanMobility/submit.sh
+++ b/examples/HumanMobility/submit.sh
@@ -3,31 +3,36 @@
 # Submit a job to run the Human Mobility example with specified mode and resources
 # ./submit.sh --run -N 1 -n 2 -c 64          # Run on COSMA5 (default, you can append "-p cosma5" optionally)
 # ./submit.sh --run -N 1 -n 2 -c 64 -p cosma # Run on COSMA (legacy hardware)
+# ./submit.sh --gen -b 50000 -g 5000 -n 500  # Generate with custom parameters
+# ./submit.sh --vis -d 1-4-16-cosma          # Visualize specific run results
+# ./submit.sh --map -d 1-4-16-cosma          # Generate map with specific suffix
 # Note: The script can be extended to other partitions like Cosma7/Cosma8
 
 # Default values
 nodes=1
 ntasks=1
-cpus_per_task=64
+cpus_per_task=64  # Default for COSMA5, will be adjusted for COSMA
 partition="cosma5"  # Default to cosma5, currently implemented for cosma5|cosma
 mode=""
 
 # Parse command line arguments
+mode_args=()  # Array to store mode-specific arguments
 while [[ $# -gt 0 ]]; do
     case $1 in
         --gen|--vis|--map|--run|--likwid|--maqao|--aps|--vtune|--advisor|--inspector|--scorep)
             mode=$1
             shift
             ;;
-        -N)
+        # SLURM-specific options only
+        -N|--nodes)
             nodes="$2"
             shift 2
             ;;
-        -n)
+        -n|--ntasks)
             ntasks="$2"
             shift 2
             ;;
-        -c)
+        -c|--cpus-per-task)
             cpus_per_task="$2"
             shift 2
             ;;
@@ -35,20 +40,43 @@ while [[ $# -gt 0 ]]; do
             partition="$2"
             shift 2
             ;;
+        -h|--help)
+            echo "Usage: $0 --TYPE [SLURM_OPTIONS] [-- MODE_SPECIFIC_OPTIONS]"
+            echo ""
+            echo "Types:"
+            echo "  gen, vis, map, run, likwid, maqao, aps, vtune, advisor, inspector, scorep"
+            echo ""
+            echo "SLURM Options:"
+            echo "  -N, --nodes NODES           Number of nodes (default: 1)"
+            echo "  -n, --ntasks NTASKS         Number of MPI tasks (default: 1)"
+            echo "  -c, --cpus-per-task CPUS    CPUs per task (default: 64 for cosma5, 16 for cosma)"
+            echo "  -p, --partition PARTITION   Partition: cosma5 (default), cosma"
+            echo ""
+            echo "Mode-specific options should be passed after -- or directly to the mode scripts"
+            echo ""
+            echo "Examples:"
+            echo "  $0 --run -N 2 -n 4 -c 32"
+            echo "  $0 --gen -- -b 50000 -g 5000 -n 500"
+            echo "  $0 --vis -- -d 1-4-16-cosma"
+            exit 0
+            ;;
+        --)
+            # Everything after -- goes to mode-specific script
+            shift
+            mode_args=("$@")
+            break
+            ;;
         *)
-            echo "Unknown option: $1"
-            echo "Usage: $0 --TYPE [-N nodes] [-n ntasks] [-c cpus-per-task] [-p partition]"
-            echo "Types: gen, vis, map, run, likwid, maqao, aps, vtune, advisor, inspector, scorep"
-            echo "Partitions: cosma5 (default), cosma"
-            exit 1
+            # Unknown options go to mode-specific script
+            mode_args+=("$1")
+            shift
             ;;
     esac
 done
 
 if [ -z "$mode" ]; then
-    echo "Usage: $0 --TYPE [-N nodes] [-n ntasks] [-c cpus-per-task] [-p partition]"
+    echo "Usage: $0 --TYPE [SLURM_OPTIONS] [-- MODE_SPECIFIC_OPTIONS]"
     echo "Types: gen, vis, map, run, likwid, maqao, aps, vtune, advisor, inspector, scorep"
-    echo "Partitions: cosma5 (default), cosma"
     exit 1
 fi
 
@@ -62,6 +90,11 @@ case $partition in
         ;;
 esac
 
+# Adjust default cpus_per_task based on partition if not explicitly set
+if [[ "$partition" == "cosma" ]] && [[ "$cpus_per_task" == "64" ]]; then
+    cpus_per_task=16
+fi
+
 name=${mode#--}  # Remove leading -- from mode
 
 # Calculate memory per node (estimate: 4GB per core)
@@ -71,14 +104,19 @@ echo "Submitting to partition: $partition"
 echo "Using unified job script: job.sh"
 echo "Resources: $nodes nodes, $ntasks tasks, $cpus_per_task cores/task"
 
-# Submit job with specified resources and partition - now always use job.sh
+# Show mode-specific arguments if any
+if [ ${#mode_args[@]} -gt 0 ]; then
+    echo "Mode-specific arguments: ${mode_args[*]}"
+fi
+
+# Submit job with specified resources and partition
 sbatch --job-name="hm-$name" \
-       --output="hm-$name-$partition-rank%t.out" \
-       --error="hm-$name-$partition-rank%t.err" \
+       --output="hm-$name-%N-rank%t-$nodes-$ntasks-$cpus_per_task-$partition.out" \
+       --error="hm-$name-%N-rank%t-$nodes-$ntasks-$cpus_per_task-$partition.err" \
        --partition="$partition" \
        --nodes=$nodes \
        --ntasks=$ntasks \
        --ntasks-per-node=$((ntasks / nodes)) \
        --cpus-per-task=$cpus_per_task \
        --mem=$mem_per_node \
-       job.sh $mode
\ No newline at end of file
+       job.sh $mode "${mode_args[@]}"
\ No newline at end of file
diff --git a/examples/HumanMobility/vis.sh b/examples/HumanMobility/vis.sh
new file mode 100755
index 000000000..46c9ea733
--- /dev/null
+++ b/examples/HumanMobility/vis.sh
@@ -0,0 +1,142 @@
+#!/bin/bash
+
+# Default parameters
+dir_suffix=""
+num_files=18
+min_x=4000
+max_x=6000
+min_y=4000
+max_y=6000
+type="gas"
+NUM_HUMANS=1000
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --num-humans)
+            NUM_HUMANS="$2"
+            shift 2
+            ;;
+        -d|--dir-suffix)
+            dir_suffix="$2"
+            shift 2
+            ;;
+        --num-files)
+            num_files="$2"
+            shift 2
+            ;;
+        --min-x)
+            min_x="$2"
+            shift 2
+            ;;
+        --max-x)
+            max_x="$2"
+            shift 2
+            ;;
+        --min-y)
+            min_y="$2"
+            shift 2
+            ;;
+        --max-y)
+            max_y="$2"
+            shift 2
+            ;;
+        --type)
+            type="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --num-humans NUM_HUMANS     Number of humans per dimension (default: 1000)"
+            echo "  -d, --dir-suffix <suffix>   Suffix part of directory path to read data from"
+            echo "  --num-files <num>           Number of output files to process (default: 18)"
+            echo "  --min-x <value>             Minimum x coordinate for visualization (default: 4000)"
+            echo "  --max-x <value>             Maximum x coordinate for visualization (default: 6000)"
+            echo "  --min-y <value>             Minimum y coordinate for visualization (default: 4000)"
+            echo "  --max-y <value>             Maximum y coordinate for visualization (default: 6000)"
+            echo "  --type <type>               Visualization type: gas or particles (default: gas)"
+            echo "  -h, --help                  Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use -h or --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# If no dir_suffix provided, auto-generate from SLURM parameters
+if [ -z "$dir_suffix" ]; then
+    nodes=${SLURM_JOB_NUM_NODES:-1}
+    ntasks=${SLURM_NTASKS:-1}
+    cpus_per_task=${SLURM_CPUS_PER_TASK:-16}
+    
+    # Determine partition suffix
+    partition_suffix=""
+    if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]]; then
+        partition_suffix="-cosma"
+    fi
+    
+    dir_suffix="${nodes}-${ntasks}-${cpus_per_task}${partition_suffix}"
+else
+    # Need to determine partition suffix for filename generation
+    partition_suffix=""
+    if [[ "${SLURM_JOB_PARTITION}" == "cosma" ]] || [[ "$dir_suffix" == *"-cosma" ]]; then
+        partition_suffix="-cosma"
+    fi
+fi
+
+# Basenames for this run (without extensions)
+HUMANS=humans-rivers-${NUM_HUMANS}${partition_suffix}
+RIVERS=river-rivers-${NUM_HUMANS}${partition_suffix}
+HUMANMOBILITY=humanMobility
+DATA=data-rivers-${dir_suffix}
+IMAGES=images-rivers-${dir_suffix}
+
+echo "Visualization Configuration:"
+echo "  Directory suffix: ${dir_suffix}"
+echo "  Data directory: ${DATA}"
+echo "  Images directory: ${IMAGES}"
+echo "  Number of files: ${num_files}"
+echo "  Visualization bounds: x=[${min_x}, ${max_x}], y=[${min_y}, ${max_y}]"
+echo "  Visualization type: ${type}"
+
+# Create images directory if it doesn't exist
+mkdir -p ${IMAGES}
+
+# Remove existing images
+rm -f ${IMAGES}/* # don't remore if locally
+
+# Check if data directory exists
+if [ ! -d "${DATA}" ]; then
+    echo "Error: Data directory '${DATA}' not found!"
+    echo "Make sure you have run a simulation first or specify the correct directory suffix with -d"
+    exit 1
+fi
+
+echo "Processing ${num_files} output files..."
+
+# Use the new configurable parameters
+python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type} ${RIVERS} ${DATA}/${HUMANMOBILITY} ${IMAGES}/${HUMANMOBILITY}
+
+echo "Visualization complete! Images saved to: ${IMAGES}/"
+
+# Optional: Generate video (commented out by default)
+# python3 generate_GIF.py ${num_files}
+
+# This command sets:
+# - A framerate of 20 frames per second.
+# - The input pattern to 'humanMobility_%04d.png' starting from 'humanMobility_0000.png'.
+# - The total number of frames to 75 (covering humanMobility_0000.png to humanMobility_0074.png).
+# - The output video format to H.264 with yuv420p pixel format for broader compatibility.
+
+# ffmpeg -framerate 100 \ # uncomment this line if locally
+#        -start_number 0 \
+#        -i ${IMAGES}/humanMobility_%04d.png \
+#        -frames:v ${num_files} \
+#        -y \
+#        -c:v libx264 \
+#        -pix_fmt yuv420p \
+#        ${IMAGES}/video.mp4
diff --git a/examples/HumanMobility/visualise.sh b/examples/HumanMobility/visualise.sh
deleted file mode 100755
index 4290d538a..000000000
--- a/examples/HumanMobility/visualise.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-
-rm images-rivers-3/* # don't remove the images directory if locally
-
-num_files=18
-min_x=4000
-max_x=6000
-min_y=4000
-max_y=6000
-type="gas"  # or "particles"
-river_base="river-rivers-3"
-data_base="data-rivers-3/humanMobility"
-image_base="images-rivers-3/humanMobility"
-
-# Plot the result
-python3 plot_velocity_parallel.py ${num_files} ${min_x} ${max_x} ${min_y} ${max_y} ${type} ${river_base} ${data_base} ${image_base}
-# python3 generate_GIF.py ${num_files}
-
-# This command sets:
-# - A framerate of 20 frames per second.
-# - The input pattern to 'humanMobility_%04d.png' starting from 'humanMobility_0000.png'.
-# - The total number of frames to 75 (covering humanMobility_0000.png to humanMobility_0074.png).
-# - The output video format to H.264 with yuv420p pixel format for broader compatibility.
-
-# ffmpeg -framerate 100 \ # uncomment this line if locally
-#        -start_number 0 \
-#        -i cosma/images/humanMobility_%04d.png \
-#        -frames:v ${num_files} \
-#        -y \
-#        -c:v libx264 \
-#        -pix_fmt yuv420p \
-#        cosma/video.mp4

From 4515f2325dc7b085f68a2566b424e81554327451 Mon Sep 17 00:00:00 2001
From: Dmitry Nikolaenko <dmitry.nikolaenko@durham.ac.uk>
Date: Fri, 27 Jun 2025 17:27:01 +0100
Subject: [PATCH 42/43] Minor update in job scheduling doc

---
 examples/HumanMobility/doc/job_scheduling.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/HumanMobility/doc/job_scheduling.md b/examples/HumanMobility/doc/job_scheduling.md
index 35b7e12ff..f2cad1ef9 100644
--- a/examples/HumanMobility/doc/job_scheduling.md
+++ b/examples/HumanMobility/doc/job_scheduling.md
@@ -1,4 +1,4 @@
-## A SLURM job scheduling suite
+## SLURM job scheduling suite
 
 ### Scripts
 

From 5d6ffe145b6e84db40be31ac4fe7daa4bc128d79 Mon Sep 17 00:00:00 2001
From: Kyle Oman <kyle.a.oman@durham.ac.uk>
Date: Mon, 30 Jun 2025 20:43:26 +0100
Subject: [PATCH 43/43] Use a long long to store grid size to avoid overflow.

---
 src/abm/Geography/river/potential.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/abm/Geography/river/potential.h b/src/abm/Geography/river/potential.h
index 096bd422b..124890bbe 100644
--- a/src/abm/Geography/river/potential.h
+++ b/src/abm/Geography/river/potential.h
@@ -181,10 +181,10 @@ static INLINE void geography_read_acceleration_field(
   if (status < 0) error("error closing group.");
 
   // Allocate and read acceleration fields
-  const int size = potential->grid_size[0] * potential->grid_size[1];
+  const long long size = potential->grid_size[0] * potential->grid_size[1];
   potential->ax = (float*)malloc(size * sizeof(float));
   potential->ay = (float*)malloc(size * sizeof(float));
-  printf("size: %d\n", size);
+  printf("acceleration grid size: %lld\n", size);
 
   group_id = H5Gopen(file_id, "AccelerationField", H5P_DEFAULT);
   if (group_id < 0) error("unable to open group AccelerationField.\n");