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Awesome GitHub RepositoriesGPU Kernel Differentiators
Automatically computes gradients through GPU kernel code for gradient-based optimization.
Distinct from Differentiable Programming: Distinct from Differentiable Programming: focuses on gradient computation through GPU kernel code specifically, not general program differentiation.
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Awesome GPU Kernel Differentiators 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
Generates backward-mode gradient computations for tile-based kernels, supporting in-place addition and subtraction.
Pythoncudadifferentiable-programminggpu
