{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mpi-rical-data-driven-mpi-distributed","title":"MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers","arxiv_id":"2305.09438","date":"2023-05-16","proceeding":null,"authors":["Nadav Schneider","Tal Kadosh","Niranjan Hasabnis","Timothy Mattson","Yuval Pinter","Gal Oren"],"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","url_abs":"https://arxiv.org/abs/2305.09438v3","url_pdf":"https://arxiv.org/pdf/2305.09438v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mpi-rical-data-driven-mpi-distributed","repo_url":"https://github.com/scientific-computing-lab-nrcn/mpi-rical","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mpi-rical-data-driven-mpi-distributed","repo_url":"https://github.com/scientific-computing-lab-nrcn/mpi-rigen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"code-completion","task_name":"Code Completion"},{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"code-translation","task_name":"Code Translation"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}