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High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

Authors:

William S. Moses,

Ivan R. Ivanov,

Johannes Doerfert,

Oleksandr ZinenkoAuthors Info & Claims

PPoPP '23: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

Pages 119 - 134

https://doi.org/10.1145/3572848.3577475

Published: 21 February 2023 Publication History

Abstract

While parallelism remains the main source of performance, architectural implementations and programming models change with each new hardware generation, often leading to costly application re-engineering. Most tools for performance portability require manual and costly application porting to yet another programming model.

We propose an alternative approach that automatically translates programs written in one programming model (CUDA), into another (CPU threads) based on Polygeist/MLIR. Our approach includes a representation of parallel constructs that allows conventional compiler transformations to apply transparently and without modification and enables parallelism-specific optimizations. We evaluate our framework by transpiling and optimizing the CUDA Rodinia benchmark suite for a multi-core CPU and achieve a 58% geomean speedup over handwritten OpenMP code. Further, we show how CUDA kernels from PyTorch can efficiently run and scale on the CPU-only Supercomputer Fugaku without user intervention. Our PyTorch compatibility layer making use of transpiled CUDA PyTorch kernels outperforms the PyTorch CPU native backend by 2.7×.

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Cited By

Zhang YLu LYang ZLiang ZSuo S(2025)A load-balanced acceleration method for small and irregular batch matrix multiplication on GPUJournal of Systems Architecture10.1016/j.sysarc.2025.103341160(103341)Online publication date: Mar-2025
https://doi.org/10.1016/j.sysarc.2025.103341
Liang HDeng CZhang PFang JTang THuang C(2025)An empirical performance evaluation of SYCL on ARM multi-core processorsCCF Transactions on High Performance Computing10.1007/s42514-024-00212-z7:1(1-16)Online publication date: 14-Feb-2025
https://doi.org/10.1007/s42514-024-00212-z
Han RChen JGarg BZhou XLu JYoung JSim JKim H(2024)CuPBoP: Making CUDA a Portable LanguageACM Transactions on Design Automation of Electronic Systems10.1145/365994929:4(1-25)Online publication date: 21-Jun-2024
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Index Terms

High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
2. Theory of computation
  1. Models of computation
    1. Concurrency
      1. Parallel computing models

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cover image ACM Conferences

PPoPP '23: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

February 2023

480 pages

ISBN:9798400700156

DOI:10.1145/3572848

General Chair:
Maryam Mehri Dehnavi
University of Toronto
,
Program Chairs:
Milind Kulkarni
Purdue University
,
Sriram Krishnamoorthy
Google

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Published: 21 February 2023

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PPoPP '23: The 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

February 25 - March 1, 2023

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https://doi.org/10.1016/j.sysarc.2025.103341
Liang HDeng CZhang PFang JTang THuang C(2025)An empirical performance evaluation of SYCL on ARM multi-core processorsCCF Transactions on High Performance Computing10.1007/s42514-024-00212-z7:1(1-16)Online publication date: 14-Feb-2025
https://doi.org/10.1007/s42514-024-00212-z
Han RChen JGarg BZhou XLu JYoung JSim JKim H(2024)CuPBoP: Making CUDA a Portable LanguageACM Transactions on Design Automation of Electronic Systems10.1145/365994929:4(1-25)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1145/3659949
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