Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE

Jesun Sahariar Firoz, Franco Pellegrini, Mario Geiger, Darren Hsu, Jenna A. Bilbrey, Han-Yi Chou, Maximilian Stadler, Markus Höhnerbach, Tingyu Wang, Dejun Lin, Emine Küçükbenli, Henry W. Sprueill, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Christopher J. Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury. Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE. In Robert W. Wisniewski, Ivona Brandic, editors, Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2025, University of Notre Dame Conference Facilities, Notre Dame, IN, USA, July 20-23, 2025. ACM, 2025. [doi]

Abstract

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