High-performance, portable GPU primitives for Julia — a pure Julia implementation delivering performance competitive with optimized CUDA C++ libraries.
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Benchmarked on an NVIDIA A40 (Ampere) against optimized CUDA C++ baselines (CUB / cuBLAS). More GPUs (RTX 1000, MI300X) and raw CSV data in the KernelForge-benchmarks repo.
- 📄 Paper — arXiv:2603.18695
- 📖 Documentation — epilliat.github.io/KernelForge.jl — API reference & examples
- 🌐 Homepage — epilliat.github.io/software
- 📊 Benchmarks — KernelForge-benchmarks — raw results across GPUs
using Pkg
Pkg.add("KernelForge")using KernelForge, CUDA # or AMDGPU
x = CUDA.rand(Float32, 10^6)
# Reduction with a custom map + operator
total = KernelForge.mapreduce(abs2, +, x) # sum of squares
# Prefix scan (supports non-commutative ops)
dst = similar(x)
KernelForge.scan!(+, dst, x) # cumulative sum
# Matrix–vector product
A = CUDA.rand(Float32, 1000, 500)
v = CUDA.rand(Float32, 500)
y = KernelForge.matvec(A, v) # y ≈ A * v
# Radix sort, in place
KernelForge.sort!(x)- Map-reduce with custom functions and operators, supporting arbitrary dimensions and multidimensional arrays
- Prefix scan supporting non-commutative operations
- Matrix-vector operations with customizable element-wise and reduction operations
- Search —
findfirst,findlast,argmax,argminon GPU arrays - Vectorized copy with configurable load/store widths
- Views and strided arrays supported throughout
CUDA (NVIDIA) and AMDGPU (AMD) via weak dependencies; the backend is selected through KernelAbstractions extensions. Tested on NVIDIA A40, RTX 1000, and AMD MI300X.
KernelForge.jl is an open-source project maintained in my personal time. If this package is useful to you — especially in a production or HPC setting — you can support its development and maintenance via GitHub Sponsors.
Corporate sponsors receive priority support on issues and an acknowledgment in the documentation.
MIT