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OmpMemOpt: Optimized Memory Movement for Heterogeneous Computing

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Euro-Par 2020: Parallel Processing (Euro-Par 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12247))

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Abstract

The fast development of acceleration architectures and applications has made heterogeneous computing the norm for high-performance computing. The cost of high volume data movement to the accelerators is an important bottleneck both in terms of application performance and developer productivity. Memory management is still a manual task performed tediously by expert programmers. In this paper, we develop a compiler analysis to automate memory management for heterogeneous computing. We propose an optimization framework that casts the problem of detection and removal of redundant data movements into a partial redundancy elimination (PRE) problem and applies the lazy code motion technique to optimize these data movements. We chose OpenMP as the underlying parallel programming model and implemented our optimization framework in the LLVM toolchain. We evaluated it with ten benchmarks and obtained a geometric speedup of 2.3\(\times \), and reduced on average 50% of the total bytes transferred between the host and GPU.

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Notes

  1. 1.

    www.openmp.org/wp-content/uploads/openmp-4.5.pdf.

  2. 2.

    www.openmp.org/wp-content/uploads/openmp-4.5.pdf.

  3. 3.

    http://llvm.org/.

  4. 4.

    https://llvm.org/docs/MemorySSA.html.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Prithayan Barua, Jisheng Zhao & Vivek Sarkar

Authors
  1. Prithayan Barua
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  2. Jisheng Zhao
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  3. Vivek Sarkar
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Corresponding author

Correspondence to Prithayan Barua .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Malawski

  2. University of Warsaw, Warsaw, Poland

    Krzysztof Rzadca

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Barua, P., Zhao, J., Sarkar, V. (2020). OmpMemOpt: Optimized Memory Movement for Heterogeneous Computing. In: Malawski, M., Rzadca, K. (eds) Euro-Par 2020: Parallel Processing. Euro-Par 2020. Lecture Notes in Computer Science(), vol 12247. Springer, Cham. https://doi.org/10.1007/978-3-030-57675-2_13

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  • DOI: https://doi.org/10.1007/978-3-030-57675-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57674-5

  • Online ISBN: 978-3-030-57675-2

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