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Awesome GitHub RepositoriesParallel Kernel Module Loaders
Configures the number of worker threads that compile and load GPU kernel modules concurrently to reduce startup time.
Distinct from Module Loading Systems: Distinct from Module Loading Systems: focuses on parallel compilation and loading of GPU kernel modules, not shell module loading.
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- nvidia/warp
NVIDIA/warp
6,233Auf GitHub ansehenWarp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera
Sets the number of worker threads that compile and load kernel modules concurrently, reducing startup time on multi-core systems.
Pythoncudadifferentiable-programminggpu
