2 repositorios
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.
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.
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.