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Automated code acceleration targeting heterogeneous openCL devices

Published:10 February 2018Publication History
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Abstract

Accelerators can offer exceptional performance advantages. However, programmers need to spend considerable efforts on acceleration, without knowing how sustainable the employed programming models, languages and tools are. To tackle this challenge, we propose and demonstrate a new runtime system called HTrOP that is able to automatically generate and execute OpenCL code from sequential CPU code. HTrOP transforms suitable data-parallel loops into independent OpenCL-typical work-items and handles concrete calls to these devices through a mix of library components and application-specific OpenCL host code. Computational hotspots are identified and can be offloaded to different resources (CPU, GPGPU and Xeon Phi). We demonstrate the potential of HTrOP on a broad set of applications and are able to improve the performance by 4.3X on average.

References

  1. Marvin Damschen, Heinrich Riebler, Gavin Vaz, and Christian Plessl. 2015. Transparent offloading of computational hotspots from binary code to Xeon Phi. In Proc. Design, Automation and Test in Europe Conf. (DATE). EDA Consortium, 1078--1083. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Tobias Grosser and Torsten Hoefler. 2016. Polly-ACC Transparent compilation to heterogeneous hardware. In Proceedings of the 2016 International Conference on Supercomputing. ACM, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Simon Moll. 2011. Decompilation of LLVM IR. Master's thesis (2011).Google ScholarGoogle Scholar

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 53, Issue 1
        PPoPP '18
        January 2018
        426 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/3200691
        Issue’s Table of Contents
        • cover image ACM Conferences
          PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
          February 2018
          442 pages
          ISBN:9781450349826
          DOI:10.1145/3178487

        Copyright © 2018 Owner/Author

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 10 February 2018

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