Scalable Integration of Computational Physics Simulations with Machine Learning
DescriptionIntegration of machine learning with simulation is part of a growing trend, however, the augmentation of codes in a highly-performant, distributed manner poses a software development challenge. In this work, we explore the question of how to easily augment legacy simulation codes on high- performance computers (HPCs) with machine-learned surrogate models, in a fast, scalable manner. Initial naive augmentation attempts required significant code modification and resulted in significant slowdown. This led us to explore inference server techniques, which allow for model calls through drop-in functions. In this work, we investigated TensorFlow Serving with gRPC and RedisAI with SmartRedis for server-client implementations, where the deep learning platform runs as a persistent process on HPC compute node GPUs and the simulation makes client calls while running on CPUs. We evaluated inference performance for real gas equations of state, machine-learned boundary conditions for rotorcraft aerodynamics, and super-resolution techniques on a POWER9 supercomputer.
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TimeMonday, 14 November 20224pm - 4:10pm CST
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