MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers

• URL: http://arxiv.org/abs/2305.09438v3
• Date: Wed, 30 Aug 2023 14:56:16 GMT
• Title: MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
• Authors: Nadav Schneider, Tal Kadosh, Niranjan Hasabnis, Timothy Mattson, Yuval Pinter, Gal Oren
• Abstract summary: Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes. We develop MPI-RICAL, a data-driven programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code. We also introduce MPICodeCorpus, the first publicly available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub.
• Score: 3.2164100882807913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging, error-prone task. In this paper, we address this problem by developing MPI-RICAL, a novel data-driven, programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code. Specifically, we train a supervised language model to suggest MPI functions and their proper locations in the code on the fly. We also introduce MPICodeCorpus, the first publicly available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub. Experimental results have been done on MPICodeCorpus and more importantly, on a compiled benchmark of MPI-based parallel programs for numerical computations that represent real-world scientific applications. MPI-RICAL achieves F1 scores between 0.87-0.91 on these programs, demonstrating its accuracy in suggesting correct MPI functions at appropriate code locations.. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rical

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