The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Authors: Mathew Boyer and Wesley Brewer (HPC Modernization Program (HPCMP) User Productivity Enhancement and Training (PET), General Dynamics Information Technology Inc); Dylan Jude (US Army); and Ian Dettwiller (US Army Corps of Engineers)
Abstract: Integration 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.