MPI-RICAL: Data-Driven MPI Distributed Parallelism Assistance with Transformers
Nadav Schneider, Tal Kadosh, Niranjan Hasabnis, Timothy G. Mattson, Yuval Pinter, Gal Oren · 2023
Computational science has made rapid progress in recent years, leading to ever increasing demand for supercomputing resources. For scientific applications that leverage such resources, Message Passing Interface (MPI) plays a crucial role in enabling distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging and error-prone task.