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Shared Memory Parallelism in Modern C++ and HPX

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Abstract

Parallel programming remains a daunting challenge, from struggling to express a parallel algorithm without cluttering the underlying synchronous logic to describing which tools to employ to ensure a calculation is performed correctly. Over the years, numerous solutions have arisen, requiring new programming languages, extensions to programming languages, or adding pragmas. Support for these various tools and extensions is available to varying degrees. In recent years, the C++ standards committee has worked to refine the language features and libraries needed to support parallel programming on a single computational node. Eventually, all major vendors and compilers will provide robust and performant implementations of these standards. Until then, the HPX library and runtime provide cutting-edge implementations of the standards and proposed standards and extensions. Because of these advances, it is now possible to write high performance parallel code without custom extensions to C++. We provide an overview of modern parallel programming in C++, describing the language and library features and providing brief examples of how to use them.

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Notes

  1. https://netpbm.sourceforge.net/doc/pbm.html.

  2. https://github.com/STEllAR-GROUP/parallelnumericalintegration.

  3. https://zenodo.org/record/7515618.

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Acknowledgements

We would also like to thank Alireza Kheirkhahan and the HPC admins who support the Deep Bayou cluster at Louisiana State University.

Funding

The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure, and the Institute for Advanced Computational Science at Stony Brook University for access to the innovative high-performance Ookami computing system, which was made possible by a $5 M National Science Foundation grant (#1927880). Data availability The code for all examples is available on GitHub®Footnote 2 or Zenodo™,Footnote 3 respectively.

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Authors and Affiliations

  1. Center of Computation and Technology, Louisiana State University, Digital Media Center, Baton Rouge, LA, 70803, USA

    Patrick Diehl, Steven R. Brandt & Hartmut Kaiser

  2. Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, 70803, USA

    Patrick Diehl

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Correspondence to Patrick Diehl.

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This article is part of the topical collection “Applications and Frameworks using the Asynchronous Many Task Paradigm” guest edited by Patrick Diehl, Hartmut Kaiser, Peter Thoman, Steven R. Brandt and Ram Ramanujam.

Additional Code Listing

Additional Code Listing

The implementation of the Mandelbrot set is shown for futures in Listing 7, coroutines in Listing 8, parallel algorithms in Listing 9, and senders and receivers in Listing 10, respectively.

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Diehl, P., Brandt, S.R. & Kaiser, H. Shared Memory Parallelism in Modern C++ and HPX. SN COMPUT. SCI. 5, 459 (2024). https://doi.org/10.1007/s42979-024-02769-6

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