Demonstration of Portable Performance of Scientific Machine Learning on High Performance Computing Systems.

Khalid Hossain, Riccardo Balin,Corey Adams,Thomas D. Uram,Kalyan Kumaran,Venkatram Vishwanath, Tanima Dey, Subrata Goswami, Janghaeng Lee, Rebecca Ramer,Koichi Yamada

SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis(2023)

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摘要
With the largest datasets to date and a diverse set of discoveries to be made, the current generation of scientific analyses are well poised to utilize artificial intelligence (AI) and machine learning (ML) on high performance computing (HPC) resources. Like never before, these workflows can be written in one portable language, python, which thanks to highly-optimized ML libraries achieves excellent cross-platform performance with little to no intervention by the user. In this demonstration, we explore the performance of several scientific AI/ML applications across leading HPC resources and highlight best practices for portable performance.
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