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DOE OSTI · 2483660

Performance on HPC Platforms Is Possible Without C++

Dubey, Anshu·Ben-Nun, Tal·Chamberlain, Bradford L.·de Supinski, Bronis R.·Rouson, Damian
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Abstract

Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. In conclusion, we describe some of the presentations and discussion from the mini symposium in this article.

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97 MATHEMATICS AND COMPUTINGAdvanced FeaturesApplication Programming InterfaceC++ languagesComputational PlatformComputational modelingComputer ScienceData PacketsDebuggingFlow ControlHardwareHigh-performance ComputingInteroperabilityLoad DataMachine LearningMachine learningMessage Passing InterfaceNumerical modelsObject-orientedParallelizationProgramming LanguageProgramming ParadigmRange Of LearningScientific computingSemanticSoftwareState Machine

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Dubey, Anshu, Ben-Nun, Tal, Chamberlain, Bradford L., de Supinski, Bronis R., Rouson, Damian. 2024-04-18. Performance on HPC Platforms Is Possible Without C++. https://doi.org/10.1109/mcse.2023.3329330

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