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Computer Science > Machine Learning

arXiv:2503.19779 (cs)
[Submitted on 25 Mar 2025]

Title:PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Authors:Abhishek Ghosh, Ajay Nayak, Ashish Panwar, Arkaprava Basu
View a PDF of the paper titled PyGraph: Robust Compiler Support for
CUDA Graphs in PyTorch, by Abhishek Ghosh and 3 other authors
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    Abstract:CUDA Graphs -- a recent hardware feature introduced for
    NVIDIA GPUs -- aim to reduce CPU launch overhead by capturing and
    launching a series of GPU tasks (kernels) as a DAG. However,
    deploying CUDA Graphs faces several challenges today due to the
    static structure of a graph. It also incurs performance overhead
    due to data copy. In fact, we show a counter-intuitive result --
    deploying CUDA Graphs hurts performance in many cases.
    We introduce PyGraph, a novel approach to automatically harness
    the power of CUDA Graphs within PyTorch2. Driven by three key
    observations, PyGraph embodies three novel optimizations: it
    enables wider deployment of CUDA Graphs, reduces GPU kernel
    parameter copy overheads, and selectively deploys CUDA Graphs
    based on a cost-benefit analysis. PyGraph seamlessly integrates
    with PyTorch2's compilation toolchain, enabling efficient use of
    CUDA Graphs without manual modifications to the code. We evaluate
    PyGraph across various machine learning benchmarks, demonstrating
    substantial performance improvements over PyTorch2.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2503.19779 [cs.LG]
          (or arXiv:2503.19779v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2503.19779
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Abhishek Ghosh [view email]
[v1] Tue, 25 Mar 2025 15:47:54 UTC (1,406 KB)
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