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Awesome GitHub RepositoriesMulti-Device Kernel Launches
Launching independent GPU kernels on different CUDA devices simultaneously to parallelize sub-tasks.
Distinct from GPU Kernel Implementations: Distinct from GPU Kernel Implementations: focuses on simultaneous multi-device execution rather than kernel implementation patterns.
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Awesome Multi-Device Kernel Launches GitHub Repositories
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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
Launches independent kernels on different CUDA devices simultaneously to parallelize sub-tasks across GPUs.
Pythoncudadifferentiable-programminggpu
