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A Comparative Evaluation of Parallel Programming Python Tools for Particle-in-Cell on Symmetric Multiprocessors

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High Performance Computing (CARLA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1660))

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Abstract

The Python programming language has established itself as a popular alternative for implementing scientific computing workflows. Its massive adoption across a wide spectrum of disciplines has created a strong community that develops tools for solving complex problems in science and engineering. In particular, there are several parallel programming libraries for Python codes that target multicore processors. We aim at comparing the performance and scalability of a subset of three popular libraries (Multiprocessing, PyMP, and Torcpy). We use the Particle-in-cell (PIC) method as a benchmark. This method is an attractive option for understanding physical phenomena, specially in plasma physics. A pre-existing PIC code implementation was modified to integrate Multiprocessing, PyMP, and Torcpy. The three tools were tested on a manycore and on a multicore processor by running different problem sizes. The results obtained consistently indicate that PyMP has the best performance, Multiprocessing showed a similar behavior but with longer execution times, and Torcpy did not properly scale when increasing the number of workers. Finally, a just-in-time (JIT) alternative was studied by using Numba, showing execution time reductions of up to 43%.

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Acknowledgments

This research was partially supported by a machine allocation on Kabré supercomputer at the Costa Rica National High Technology Center.

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Authors and Affiliations

  1. School of Computing, Costa Rican Institute of Technology, Cartago, Costa Rica

    Oscar Blandino H. & Esteban Meneses

  2. Advanced Computing Laboratory, National High Technology Center, San Jose, Costa Rica

    Esteban Meneses

Authors
  1. Oscar Blandino H.
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  2. Esteban Meneses
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Corresponding author

Correspondence to Oscar Blandino H. .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Philippe Navaux

  2. Universidad Industrial de Santander, Bucaramanga, Colombia

    Carlos J. Barrios H.

  3. Laboratório Nacional de Computação Científica, Petrópolis, Brazil

    Carla Osthoff

  4. Laboratorio Nacional de Computación de Alto Rendimiento, Santiago, Chile

    Ginés Guerrero

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Blandino H., O., Meneses, E. (2022). A Comparative Evaluation of Parallel Programming Python Tools for Particle-in-Cell on Symmetric Multiprocessors. In: Navaux, P., Barrios H., C.J., Osthoff, C., Guerrero, G. (eds) High Performance Computing. CARLA 2022. Communications in Computer and Information Science, vol 1660. Springer, Cham. https://doi.org/10.1007/978-3-031-23821-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-23821-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23820-8

  • Online ISBN: 978-3-031-23821-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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