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International Workshop on Languages and Compilers for Parallel Computing

LCPC 2020: Languages and Compilers for Parallel Computing pp 41–56Cite as

Concurrent Execution of Deferred OpenMP Target Tasks with Hidden Helper Threads

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13149))

Abstract

In this paper, we introduce a novel approach to support concurrent offloading for OpenMP tasks based on hidden helper threads. We contrast our design to alternative implementations and explain why the approach we have chosen provides the most consistent performance across a wide range of use cases. In addition to a theoretical discussion of the trade-offs, we detail our implementation in the LLVM compiler infrastructure. Finally, we provide evaluation results of four extreme offloading situations on the Summit supercomputer, showing that we achieve speedup of up to \(6.7\times \) over synchronous offloading, and provide comparable speedup to the commercial IBM XL C/C++ compiler.

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Notes

  1. 1.

    The fallback case, execution on the issuing device, is sufficiently similar.

  2. 2.

    This is CUDA terminology, but almost all heterogeneous programming models have a similar concept, such as the command queue in OpenCL.

References

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  4. IBM: OpenMP support in XL C/C++. https://www.ibm.com/support/knowledgecenter/SSXVZZ_16.1.1/com.ibm.xlcpp1611.lelinux.doc/getstart/omp_v1611.html

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  9. Project, G.: Offloading support in GCC. https://gcc.gnu.org/wiki/Offloading

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Acknowledgments

This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering, and early testbed platforms, in support of the nation’s exascale computing imperative.

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Authors and Affiliations

  1. Department of Computer Science, Stony Brook University, Stony Brook, USA

    Shilei Tian & Barbara Chapman

  2. Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, USA

    Johannes Doerfert

Authors
  1. Shilei Tian
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  2. Johannes Doerfert
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  3. Barbara Chapman
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Corresponding author

Correspondence to Shilei Tian .

Editor information

Editors and Affiliations

  1. Inst for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA

    Barbara Chapman

  2. IBM TJ Watson Research Center, Yorktown Heights, NY, USA

    José Moreira

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Tian, S., Doerfert, J., Chapman, B. (2022). Concurrent Execution of Deferred OpenMP Target Tasks with Hidden Helper Threads. In: Chapman, B., Moreira, J. (eds) Languages and Compilers for Parallel Computing. LCPC 2020. Lecture Notes in Computer Science(), vol 13149. Springer, Cham. https://doi.org/10.1007/978-3-030-95953-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-95953-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95952-4

  • Online ISBN: 978-3-030-95953-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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