m4rs-mt/ILGPU

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1748 stars · 140 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ILGPU is a just-in-time compiler for high-performance GPU programs (kernels) written in .NET languages like C# and F#, entirely in C# with no native dependencies. It compiles kernels to CUDA, OpenCL, and PTX targets and includes a multi-threaded CPU accelerator for debugging and emulation.

Use cases

• write GPU kernels in C# without learning CUDA or OpenCL
• run parallel computations on NVIDIA, AMD, or Intel GPUs from .NET
• debug GPU kernels on the CPU with a .NET debugger
• accelerate math-heavy workloads like N-body simulation
• use standard parallel algorithms like sorting and prefix sums on any accelerator
• prototype GPU code portably without native dependencies

When to choose

• you want GPU acceleration from C# or F# with type-safe, unannotated kernels
• you need portable GPU code across CUDA and OpenCL vendors
• you want to debug kernels on the CPU using your normal .NET tooling
• you prefer a managed library with no native dependencies over writing C++ CUDA

When to avoid

• you need maximum raw performance from hand-tuned CUDA or low-level PTX control
• your project is not on the .NET platform
• you need mature ecosystem support like deep learning frameworks built on top
• you require vendor-specific CUDA features not exposed by the compiler

Facets

library · maturity active

compiler gpu-computing machine-learning gpu-computing developer-tools performance cross-platform windows dotnet jit-compiler cuda opencl ptx gpgpu c-sharp kernels cpu-accelerator parallel-computing linux macos nvidia amd intel

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