TY - GEN
T1 - DeepThermo
T2 - 37th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
AU - Yin, Junqi
AU - Wang, Feiyi
AU - Shankar, Mallikarjun Arjun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation - DeepThermo. By using deep learning-based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e10,000 for a real material. We also demonstrate DeepThermo's performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.
AB - Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation - DeepThermo. By using deep learning-based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e10,000 for a real material. We also demonstrate DeepThermo's performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.
KW - Deep Learning
KW - High Entropy Alloy
KW - Monte Carlo Method
UR - http://www.scopus.com/inward/record.url?scp=85166654266&partnerID=8YFLogxK
U2 - 10.1109/IPDPS54959.2023.00041
DO - 10.1109/IPDPS54959.2023.00041
M3 - Conference contribution
AN - SCOPUS:85166654266
T3 - Proceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
SP - 333
EP - 343
BT - Proceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 May 2023 through 19 May 2023
ER -