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Vecpar – A Framework for Portability and Parallelization

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14073))

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Abstract

Complex particle reconstruction software used by High Energy Physics experiments already pushes the edges of computing resources with demanding requirements for speed and memory throughput, but the future experiments pose an even greater challenge. Although many supercomputers have already reached petascale capacities using many-core architectures and accelerators, numerous scientific applications still need to be adapted to make use of these new resources. To ensure a smooth transition to a platform-agnostic code base, we developed a prototype of a portability and parallelization framework named vecpar. In this paper, we introduce the technical concepts, the main features and we demonstrate the framework’s potential by comparing the runtimes of the single-source vecpar implementation (compiled for different architectures) with native serial and parallel implementations, which reveal significant speedup over the former and competitive speedup versus the latter. Further optimizations and extended portability options are currently investigated and are therefore the focus of future work.

This work was supported by DASHH under grant number HIDSS-0002.

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Notes

  1. 1.

    A track is a charged particle trajectory through a detector.

  2. 2.

    https://github.com/wr-hamburg/vecpar.

  3. 3.

    As an exception motivated by performance optimization purposes, the vecpar API provides a limited number of mutable versions as well.

  4. 4.

    A parallel_mmap operator which allows mutable data structures is also defined.

  5. 5.

    https://github.com/wr-hamburg/BabelStream/tree/vecpar.

  6. 6.

    A set of parameters describing the helical trajectory followed by a charged particle moving within a magnetic field.

  7. 7.

    TARGET is a vecpar macro which adds extra qualifiers at compile time.

  8. 8.

    While the initial goal was to contribute to the increase of the performance portability of open-source track reconstruction software, other scientific use cases are welcome in the future.

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Acknowledgment

We acknowledge the support by DASHH (Data Science in Hamburg - HELMHOLTZ Graduate School for the Structure of Matter) with the Grant-No. HIDSS-0002. The National Analysis Facility (NAF) at Deutsches Elektronen-Synchrotron (DESY), the University of Hamburg (UHH) and Deutsche Klimarechenzentrum (DKRZ) provided the hardware resources for the experiments.

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Authors and Affiliations

  1. Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607, Hamburg, Germany

    Georgiana Mania & Nicholas Styles

  2. University of Hamburg, Hamburg, Germany

    Georgiana Mania & Thomas Ludwig

  3. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Michael Kuhn

  4. CERN, 1211, Geneva, Switzerland

    Andreas Salzburger

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Beomki Yeo

  6. Department of Physics, University of California, Berkeley, CA, 94720, USA

    Beomki Yeo

  7. Deutsches Klimarechenzentrum, Bundesstraße 45a, 20146, Hamburg, Germany

    Thomas Ludwig

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  1. Georgiana Mania
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Correspondence to Georgiana Mania .

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Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

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

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Mania, G., Styles, N., Kuhn, M., Salzburger, A., Yeo, B., Ludwig, T. (2023). Vecpar – A Framework for Portability and Parallelization. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-35995-8_18

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