- Fix curl global init thread safety: use singleton CurlGlobalManager
- Fix cross-rack detection: use global_rank_rack_map_ instead of gpus_per_server_
- Initialize WorkloadConfig members with default values
- Optimize dependency tracking from O(n²) to O(n) using map lookup
- Add error return values to OxcFlowOutput functions
- Rename static debug counters for clarity
- Add DP workload test file
- Update design document with Mermaid diagrams

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

- Remove git submodules (SimCCL/aicb/ns-3-alibabacloud) — all code in one repo
- Add ranks field to LogItem CSV output with participating GPU rank IDs
- Add sidecar _rank_mapping.csv with full rank group decomposition
- Add rank_mapper.py: CommGroup-to-RankGenerator token bridge (7 group types)
- Add _fill_ranks() in WorkloadGenerator for automatic rank population
- Add Domain Flow Graph + Domain MsgSize Bar visualization charts
- Add per-rank CSV generation script (generate_per_rank_csv.py)
- Add 15 unit tests (rank_mapper + LogItem serialization)
- Fix LRA gate to support multiple concurrent in_progress features
- Deep Interview spec + Ralplan consensus plan artifacts

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

v3 field mapping: node_ip→node_id, a_node_ip→a_node_id,
port_infos→port_id_list, port_name→port_id, server_type→chassis_topo

merger.py: N:N topology (OXC→spine→leaf fan-out, server→N leaves)
ns3_emitter.py: bandwidth from port_id (800GE→800Gbps), NPU from chassis_topo
edg_client.py: spine-aware mock crosses and smart adjustment
HomePage.tsx: frontend v3 auto-detect (server IP, bandwidth, NPU type)
lld_to_topology.py: v3 visualization with IP-based cell IDs
SimAI.conf: /etc→/tmp paths, +800Gbps rate map
SimAI_training_workload_generator.py: fix get_model_details() model→self.model

Tests: 99/99 pass, NS3 verified 8/16/32 GPU ALLREDUCE with AIOB workload

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Replace spine_to_leaves[spine_ip] with spine_port_to_leaf[(spine_ip, spine_port)]
for exact per-port edge resolution from LLDP data. No hardcoded formulas.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

…lient

Apply re.sub(r"\(\d+\)$", "", node_id) in _build_edge_maps, resolve_paths,
_mock_baseline_crosses, and _smart_adjustment so OXC node_id "IP(0)"
matches edge a_node_id "IP(0)" regardless of format.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Migrate lld.json/init_crosses.json from per-session workspace to global
EDG_DATA_ROOT/{topology_dir}/ storage. wizard-store add zustand/persist
for EDG graph data survival across page refreshes.

- server/config.py: add EDG_DATA_ROOT config
- server/edg/routes.py: _edg_global_dir() + _edg_load() with global-first,
  workspace-fallback strategy. init writes to both stores.
- edg-api.ts + EdgPage.tsx: pass topologyDir to baseline-graph/register-task
- wizard-store.ts: persist EDG graph data to localStorage
- feature_list.json: F091 added, F090 marked done

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

v3 lld server node_id is a name (superpod#0_server#0), not an IP.
Rename across 10 files: frontend types/stores/api/pages + backend
routes/merger/tests. npu_match server_ip field preserved (external
EDG protocol contract).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

- network-store setActiveNetwork now resets wizard store and clears
  ocs-sim-wizard localStorage to prevent stale graph data leakage
- lld_to_topology.py now detects group_id from lld.json and generates
  per-group pod XMLs instead of 1 pod per input file (8 pods for 8 groups)
- Fix pre-existing ntype→node_type variable reference bug in
  generate_pod_xml / generate_pod_xml_with_crosses

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Major changes:
- lld.json: rewrite to 8NPU×8port per server topology (64 leaf, 512 edges)
- ns3_emitter.py: each NPU→all leaves (not round-robin 1:1)
- merger.py: fix IP string sort→numeric sort for leaf ordering
- F091: EDG init global persistence (EDG_DATA_ROOT)
- F092: serverIps→serverIds rename (v3 lld uses names not IPs)
- network-store.ts: localStorage migration + network switch reset
- routes.py: global EDG store + server_ids params
- Various OXC/NS3 C++ fixes from previous sessions

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

- ns3_emitter.py: add unfolded mode (explicit spine/OXC switch nodes)
  via optional lld param. Leaf→Spine + Spine→OXC replaces Leaf↔Leaf.
  Backward compatible: no lld = folded mode.
- merger.py: fix IP string sort→numeric sort for leaf ordering
- lld.json: rewrite to 8NPU×8port×8leaf per server topology
- F091: EDG init global persistence (EDG_DATA_ROOT)
- F092: serverIps→serverIds rename + localStorage migration

Unfolded topology: 35 nodes (16NPU+16Leaf+2Spine+1OXC), 202 links.
Cross-server path: NPU→Leaf→Spine→OXC→Spine→Leaf→NPU (7 hops).
Single OXC avoids NS3 multi-path routing loops.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

…ment + WorkloadPage stale EDG cleanup

- ProcessList: add View cmd / View error expandable buttons with
  return_code + error_message display, color-coded status badges
- ns3_emitter: raise RuntimeError when lld has spine/OXC but unfolding
  produces zero links — no more silent fallback to folded mode
- WorkloadPage: clear edgTopologyPath/BaselineGraph/AdjustedGraph/Diff
  on new workload generation to prevent stale EDG topology leak

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

… inference, CP, Chakra, tensor graph

Complete AICB workload generator extensibility implementation (F093-F099):

F093: Model registry -- MODEL_REGISTRY dict + _bootstrap.py registration
      replaces hardcoded if/elif dispatch in generate_megatron_workload.py
F094: LLaMA MockedModel -- MockedLlama.py (539 lines): GQA + SwiGLU + RMSNorm
      pre-norm, reuses MegatronColumn/RowLinear for TP. Supports LLaMA
      2/3/4 configs (7B through 70B, dense and MoE).
F095: Parameterized MoE routing -- --n_shared_expert moved from DeepSeek-only
      to get_moe_params (all MoE models). MOEMLP shared expert computation.
F096: Qwen3 inference -- MockedQwen3Moe.py (344 lines, 8 classes) +
      MockedQwen3Next.py (287 lines, 4 classes + GatedDeltaNet).
F097: Context Parallelism -- CommGroup.cp_group + ContextParallelRing (110
      lines) for ring-attention isend/irecv between CP neighbors.
F098: Chakra output format -- ChakraWriter (178 lines) converts AICB LogItem
      to MLCommons Chakra JSON schema (COMP_ONLY/COMM_COLL/COMM_SEND/COMM_RECV).
F099: Declarative tensor graph -- tensor_graph package (345 lines): TensorGraph
      CSV load/dump, ReplicateGraph layer stacking, ConnectGraph port wiring.
      SwiGLU FFN 8-line CSV template as proof-of-concept.

Also: registry.py (119 lines) + _bootstrap.py (103 lines) infrastructure,
      --num_kv_heads CLI arg for GQA architectures,
      test_registry.py and test_mocked_llama.py test files.

Research: research_aicb_extensibility.md (491 lines) -- STAGE paper analysis,
          PARAM/Chakra comparison, 2025-2026 model parallel strategy survey.

21 files, +3662/-130

Co-Authored-By: Claude <noreply@anthropic.com>

…g, Qwen3 inference, CP, Chakra, tensor graph"

This reverts commit 8604a3c.

- Fetch simulation progress via fetchProgress API for running processes
- Show progress bar with percentage, layer count, and ETA
- Extract and display workload filename from command line (-w argument)
- Restructure layout into two-row format (status+PID+buttons / progress bar)
- Add formatETA and extractWorkloadName helper functions

Co-Authored-By: Claude <noreply@anthropic.com>

All three use LLaMA-compatible architecture (RMSNorm + SwiGLU + GQA + RoPE),
reuse existing MegatronModel workload generator.
Verified parameters from deep-research HF config.json analysis.

Co-Authored-By: Claude <noreply@anthropic.com>

…letion-based timing

Adds flow recording instrumentation for NS3 decoupled replay:
- MockNcclGroup: flow buffer accumulation, send-time & completion-time recording,
  deferred finalizeFlowFile() with completion-based relative_delay_ns
- AstraSimNetwork.cc: recordFlowSendTime in sim_send, explicit finalize in main
- Sys.cc: finalizeFlowFile call in destructor (analytical mode safety net)
- common.h: relative_delay_ns field in FlowRecord
- scripts/check-common-h-consistency.sh: CI diff-check for 3 common.h copies

relative_delay_ns = send_time - max(prev completion times), clamped to 0.
No flow-level dependency graph -- causality fully encoded in timing.

Co-Authored-By: Claude <noreply@anthropic.com>

Independent binary (scratch/decoupled_replay/): 8 files, 2,596 lines
- Zero SimAI linkage (nm check target)
- DepScheduler with prev[] dependency graph + layer_num constraint
- flow_reader.h parses complete 21-field format
- Whitelisted in scratch/.gitignore
CI: scripts/check-common-h-consistency.sh

Co-Authored-By: Claude <noreply@anthropic.com>

Plan and all documentation reference scratch_decoupled_replay. CMakeLists.txt
had scratch_decoupled_replay_main which would break nm verification commands.

Co-Authored-By: Claude <noreply@anthropic.com>

prev[] contains rank IDs (0,1,2...) but _flow_completion_times is keyed
by flow ID (10423,10424...). The naive _flow_completion_times.count(pid)
always failed for ring allreduce flows, causing every flow to fall back
to relative_delay_ns = absolute send_time.

Build a per-rank sorted map of (flow_id, completion_time) pairs, then
resolve each prev rank ID to the most recent predecessor flow from that
rank via lower_bound. This correctly computes relative_delay_ns as
send_time - max(predecessor completion times).

Co-Authored-By: Claude <noreply@anthropic.com>

Cherry-picked reverted commits from submodule reflog:
- feat: decoupled replay Phase 2 (1598bbc)
- fix: GPUType enum, fct_writer format string (f0e19bb)
- refactor: inline SendFlow, remove _QPS_PER_CONNECTION_ (6843092)
- fix: sequential step numbering in main.cc (64a3613)

prev[] contains rank IDs (0,1,2...) but _flow_completion_times is keyed
by flow ID (10423,10424...). The naive _flow_completion_times.count(pid)
always failed for ring allreduce flows, causing every flow to fall back
to relative_delay_ns = absolute send_time.

Build a per-rank sorted map of (flow_id, completion_time) pairs, then
resolve each prev rank ID to the most recent predecessor flow from that
rank via lower_bound. This correctly computes relative_delay_ns as
send_time - max(predecessor completion times).

Co-Authored-By: Claude <noreply@anthropic.com>