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The Productivity, Portability and Performance of OpenMP 4.5 for Scientific Applications Targeting Intel CPUs, IBM CPUs, and NVIDIA GPUs

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Book cover Scaling OpenMP for Exascale Performance and Portability (IWOMP 2017)

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Abstract

This research considers the productivity, portability, and performance offered by the OpenMP parallel programming model, from the perspective of scientific applications. We discuss important considerations for scientific application developers tackling large software projects with OpenMP, including straightforward code mechanisms to improve productivity and portability. Performance results are presented across multiple modern HPC devices, including Intel Xeon, and Xeon Phi CPUs, POWER8 CPUs, and NVIDIA GPUs. The results are collected for three exemplar applications: hydrodynamics, heat conduction and neutral particle transport, using modern compilers with OpenMP support. The results show that while current OpenMP implementations are able to achieve good performance on the breadth of modern hardware for memory bandwidth bound applications, our memory latency bound application performs less consistently.

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Notes

  1. 1.

    https://github.com/uob-hpc/arch.

  2. 2.

    https://github.com/clang-ykt.

  3. 3.

    https://github.com/uob-hpc/flow.

  4. 4.

    https://github.com/uob-hpc/hot.

  5. 5.

    https://github.com/uob-hpc/neutral.

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Acknowledgements

The authors would like to thank the EPSRC for funding this research. We would also like to thank the Intel Parallel Computing Center (IPCC) at the University of Bristol for access to Intel hardware, and the EPSRC GW4 Tier 2 Isambard service for access to phase 1 of the Isambard supercomputer.

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Authors and Affiliations

  1. HPC Group, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS81UB, UK

    Matt Martineau & Simon McIntosh-Smith

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  1. Matt Martineau
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  2. Simon McIntosh-Smith
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Corresponding author

Correspondence to Matt Martineau .

Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Livermore, California, USA

    Bronis R. de Supinski

  2. Sandia National Laboratories, Albuquerque, New Mexico, USA

    Stephen L. Olivier

  3. RWTH Aachen University, Aachen, Germany

    Christian Terboven

  4. Stony Brook University, Stony Brook, New York, USA

    Barbara M. Chapman

  5. RWTH Aachen University, Aachen, Germany

    Matthias S. Müller

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Martineau, M., McIntosh-Smith, S. (2017). The Productivity, Portability and Performance of OpenMP 4.5 for Scientific Applications Targeting Intel CPUs, IBM CPUs, and NVIDIA GPUs. In: de Supinski, B., Olivier, S., Terboven, C., Chapman, B., Müller, M. (eds) Scaling OpenMP for Exascale Performance and Portability. IWOMP 2017. Lecture Notes in Computer Science(), vol 10468. Springer, Cham. https://doi.org/10.1007/978-3-319-65578-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-65578-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65577-2

  • Online ISBN: 978-3-319-65578-9

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