PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch
The paper presents PyGraph, which enhances CUDA Graph deployment in PyTorch by optimizing performance issues, reducing overhead, and integrating with the compilation toolchain, showing significant improvements over PyTorch2.
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The paper titled "PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch" introduces a new approach to enhance the deployment of CUDA Graphs within the PyTorch framework. CUDA Graphs, a feature for NVIDIA GPUs, aim to minimize CPU launch overhead by capturing and executing a series of GPU tasks as a directed acyclic graph (DAG). However, the static nature of these graphs can lead to performance issues, including data copy overheads that may degrade performance. The authors present PyGraph, which incorporates three key optimizations: it broadens the deployment of CUDA Graphs, reduces the overhead associated with GPU kernel parameter copying, and selectively implements CUDA Graphs based on a cost-benefit analysis. PyGraph integrates seamlessly with PyTorch's compilation toolchain, allowing for efficient utilization of CUDA Graphs without requiring manual code adjustments. The evaluation of PyGraph across various machine learning benchmarks indicates significant performance improvements compared to the existing PyTorch2 framework.
- PyGraph enhances the deployment of CUDA Graphs in PyTorch.
- It addresses performance issues related to static graph structures and data copying.
- The approach includes optimizations for broader deployment and reduced overhead.
- PyGraph integrates with PyTorch's compilation toolchain for ease of use.
- Performance evaluations show substantial improvements over PyTorch2.
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6 commentsSomething worth exploring later would be getting better support for the rest of CUDA graphs into PyTorch, like conditional nodes.
Uday Bondhugula, the lead developer of Pluto framework for polyhedral comp. is also at IISc, whose group has spun out a startup,
Nice to see IISc support cool stuff like this (incl. their ArtPark initiative.)
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