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Message Passing Interface (MPI) plays a crucial role in distributed memoryparallelization across multiple nodes. However, parallelizing MPI codemanually, and specifically, performing domain decomposition, is a challenging,error-prone task. In this paper, we address this problem by developingMPI-RICAL, a novel data-driven, programming-assistance tool that assistsprogrammers in writing domain decomposition based distributed memoryparallelization code. Specifically, we train a supervised language model tosuggest MPI functions and their proper locations in the code on the fly. Wealso introduce MPICodeCorpus, the first publicly available corpus of MPI-basedparallel programs that is created by mining more than 15,000 open-sourcerepositories on GitHub. Experimental results have been done on MPICodeCorpusand more importantly, on a compiled benchmark of MPI-based parallel programsfor numerical computations that represent real-world scientific applications.MPI-RICAL achieves F1 scores between 0.87-0.91 on these programs, demonstratingits accuracy in suggesting correct MPI functions at appropriate codelocations.. The source code used in this work, as well as other relevantsources, are available at:https://github.com/Scientific-Computing-Lab-NRCN/MPI-rical

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