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Automatic and Portable Mapping of Data Parallel Programs to OpenCL for GPU-Based Heterogeneous Systems

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Michael F. P. O’boyleAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 11, Issue 4

Article No.: 42, Pages 1 - 26

https://doi.org/10.1145/2677036

Published: 08 December 2014 Publication History

Abstract

General-purpose GPU-based systems are highly attractive, as they give potentially massive performance at little cost. Realizing such potential is challenging due to the complexity of programming. This article presents a compiler-based approach to automatically generate optimized OpenCL code from data parallel OpenMP programs for GPUs. A key feature of our scheme is that it leverages existing transformations, especially data transformations, to improve performance on GPU architectures and uses automatic machine learning to build a predictive model to determine if it is worthwhile running the OpenCL code on the GPU or OpenMP code on the multicore host. We applied our approach to the entire NAS parallel benchmark suite and evaluated it on distinct GPU-based systems. We achieved average (up to) speedups of 4.51× and 4.20× (143× and 67×) on Core i7/NVIDIA GeForce GTX580 and Core i7/AMD Radeon 7970 platforms, respectively, over a sequential baseline. Our approach achieves, on average, greater than 10× speedups over two state-of-the-art automatic GPU code generators.

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Ahmed ULin JSrivastava GMekala MJung H(2022)Fuzzy Active Learning to Detect OpenCL Kernel Heterogeneous Machines in Cyber Physical SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.316715830:11(4618-4629)Online publication date: Nov-2022
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Index Terms

Automatic and Portable Mapping of Data Parallel Programs to OpenCL for GPU-Based Heterogeneous Systems
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 11, Issue 4

January 2015

797 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/2695583

Editor:
Koen De Bosschere
Ghent University

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Copyright © 2014 ACM.

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Publication History

Published: 08 December 2014

Accepted: 01 October 2014

Revised: 01 October 2014

Received: 01 December 2013

Published in TACO Volume 11, Issue 4

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https://doi.org/10.1109/TFUZZ.2022.3167158
Moreń KGöhringer D(2021)CoopCLProceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies10.1145/3468044.3468061(1-2)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3468044.3468061
Ye GTang ZTan SHuang SFang DSun XBian LWang HWang ZFreund SYahav E(2021)Automated conformance testing for JavaScript engines via deep compiler fuzzingProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454054(435-450)Online publication date: 19-Jun-2021
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