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2 repositorios

Awesome GitHub RepositoriesCustom Kernel Compilers

Compilers that transform high-level language functions into optimized machine code for hardware acceleration.

Distinct from Compiled Numeric Functions: The candidates focus on general JIT or specific logic-to-function translation, not GPU-targeted custom function compilation.

Explore 2 awesome GitHub repositories matching programming languages & runtimes · Custom Kernel Compilers. Refine with filters or upvote what's useful.

Awesome Custom Kernel Compilers GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • iree-org/ireeAvatar de iree-org

    iree-org/iree

    3,819Ver en GitHub↗

    IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis

    Implements transformations that convert custom operations into standardized dialects to ensure compatibility with the compilation pipeline.

    C++compilercudajax
    Ver en GitHub↗3,819
  • nvidia/cuda-pythonAvatar de NVIDIA

    NVIDIA/cuda-python

    3,170Ver en GitHub↗

    cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic wrapper for controlling device memory, managing hardware toolchains, and orchestrating execution graphs on NVIDIA GPUs, allowing for the compilation and launching of parallel kernels directly from Python. The project enables the development of SIMT kernels and the execution of mathematical algorithms on device memory. It integrates pre-compiled bytecode as custom operators and interfaces with accelerated device libraries to access low-level hardware functions without leaving the la

    Transforms custom Python functions into optimized machine code to increase execution speed on the GPU.

    Cython
    Ver en GitHub↗3,170
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  3. Custom Kernel Compilers

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  • Dialect Conversion PatternsTransforms custom operations into standard dialects for compilation pipeline compatibility. **Distinct from Custom Kernel Compilers:** Focuses on IR dialect transformation rather than full kernel compilation to machine code.