The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: AI Assisted Software Development for HPC (AI4DEV)
Authors: Nadav Schneider and Tal Kadosh (Ben-Gurion University of the Negev, IAEC); Niranjan Hasabnis and Timothy Mattson (Intel Labs); Yuval Pinter (Ben-Gurion University of the Negev); and Gal Oren (Technion – Israel Institute of Technology)
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.
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