1 Repo
Awesome GitHub RepositoriesGPU Kernel Runtime Checks
Activates runtime checks for array access, CUDA errors, floating-point values, and gradient computation during GPU kernel execution.
Distinct from Development and Debugging: Distinct from general Development and Debugging: focuses on runtime verification of GPU kernel correctness, not general-purpose debugging tools.
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Awesome GPU Kernel Runtime Checks 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
Activates runtime checks for array access, CUDA errors, floating-point values, and gradient computation to catch correctness issues during development.
Pythoncudadifferentiable-programminggpu
