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DTSTAMP:20230124T171523Z
LOCATION:C144-145
DTSTART;TZID=America/Chicago:20221114T140500
DTEND;TZID=America/Chicago:20221114T142000
UID:submissions.supercomputing.org_SC22_sess462_ws_ai4s106@linklings.com
SUMMARY:A Case Study on Coupling OpenFOAM with Different Machine Learning 
 Frameworks
DESCRIPTION:Workshop\n\nA Case Study on Coupling OpenFOAM with Different M
 achine Learning Frameworks\n\nOrland, Brose, Bissantz, Ferraro, Terboven..
 .\n\nIn High-Performance Computing, new use cases are currently emerging i
 n which classical numerical simulations are coupled with machine learning 
 as a surrogate for complex physical models that are expensive to compute. 
 In the context of simulating reactive thermo-fluid systems, the idea to re
 place current state-of-the-art tabulated chemistry with machine learning\n
 inference is an active field of research. For this purpose, a simplified O
 penFOAM application is coupled with an artificial neural network. In this 
 work, we present a case study focusing solely on the performance of the co
 upled OpenFOAM-ML application. Our coupling approach features a heterogene
 ous cluster architecture combining pure CPU nodes and nodes equipped with 
 two Nvidia V100 GPUs. We evaluate our approach by comparing the inference 
 performance and the communication our approach induces with various machin
 e learning frameworks. Additionally,\nwe also compare the GPUs with NEC Ve
 ctor Engine Type 10B regarding inference performance.\n\nSession Format: R
 ecorded\n\nRegistration Category: Workshop Reg Pass
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