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Harmonic CUDA: Asynchronous Programming on GPUs

Published: 25 February 2023 Publication History

Abstract

We introduce Harmonic CUDA, a dataflow programming model for GPUs that allows programmers to describe algorithms as a dependency graph of producers and consumers where data flows continuously through the graph for the duration of the kernel. This makes it easier for programmers to exploit asynchrony, warp specialization, and hardware acceleration. Using Harmonic CUDA, we implement two example applications: Matrix Multiplication and GraphSage. The matrix multiplication kernel demonstrates how a key kernel can break down into more granular building blocks, with results that show a geomean average of 80% of cuBLAS performance, and up to 92% when omitting small matrices, as well as an analysis of how to improve performance in the future. GraphSage shows how asynchrony and warp specialization can provide significant performance improvements by reusing the same building blocks as the matrix multiplication kernel. We show performance improvements of 34% by changing to a warp-specialized version compared to a bulk-synchronous implementation. This paper evaluates the strengths and weaknesses of Harmonic CUDA based on these test cases and suggests future work to improve the programming model.

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Cited By

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  • (2024)An approach for low-power heterogeneous parallel implementation of ALC-PSO algorithm using OmpSs and CUDAParallel Computing10.1016/j.parco.2024.103084120(103084)Online publication date: Jun-2024

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    cover image ACM Conferences
    PMAM'23: Proceedings of the 14th International Workshop on Programming Models and Applications for Multicores and Manycores
    February 2023
    73 pages
    ISBN:9798400701153
    DOI:10.1145/3582514
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    New York, NY, United States

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    Published: 25 February 2023

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    Author Tags

    1. GPU
    2. CUDA
    3. programming model
    4. asynchronous
    5. GEMM
    6. graphsage

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    • (2024)An approach for low-power heterogeneous parallel implementation of ALC-PSO algorithm using OmpSs and CUDAParallel Computing10.1016/j.parco.2024.103084120(103084)Online publication date: Jun-2024

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