TY - GEN
T1 - Machine Learning-assisted Computational Steering of Large-scale Scientific Simulations
AU - Liu, Wuji
AU - Ye, Qianwen
AU - Wu, Chase Q.
AU - Liu, Yangang
AU - Zhou, Xin
AU - Shan, Yunpeng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Next-generation scientific applications in various fields are experiencing a rapid transition from traditional experiment-based methodologies to large-scale computation-intensive simulations featuring complex numerical modeling with a large number of tunable parameters. Such model-based simulations generate colossal amounts of data, which are then processed and analyzed against experimental or observation data for parameter calibration and model validation. The sheer volume and complexity of such data, the large model-parameter space, and the intensive computation make it practically infeasible for domain experts to manually configure and tune hyperparameters for accurate modeling in complex and distributed computing environments. This calls for an online computational steering service to enable real-time multi-user interaction and automatic parameter tuning. Towards this goal, we design and develop a generic steering framework based on Bayesian Optimization (BO) and conduct theoretical performance analysis of the steering service. We present a case study with the Weather Research and Forecast (WRF) model, which illustrates the performance superiority of the BO-based tuning over other heuristic methods and manual settings of domain experts using regret analysis.
AB - Next-generation scientific applications in various fields are experiencing a rapid transition from traditional experiment-based methodologies to large-scale computation-intensive simulations featuring complex numerical modeling with a large number of tunable parameters. Such model-based simulations generate colossal amounts of data, which are then processed and analyzed against experimental or observation data for parameter calibration and model validation. The sheer volume and complexity of such data, the large model-parameter space, and the intensive computation make it practically infeasible for domain experts to manually configure and tune hyperparameters for accurate modeling in complex and distributed computing environments. This calls for an online computational steering service to enable real-time multi-user interaction and automatic parameter tuning. Towards this goal, we design and develop a generic steering framework based on Bayesian Optimization (BO) and conduct theoretical performance analysis of the steering service. We present a case study with the Weather Research and Forecast (WRF) model, which illustrates the performance superiority of the BO-based tuning over other heuristic methods and manual settings of domain experts using regret analysis.
KW - Bayesian optimization
KW - Computational steering
KW - Machine learning
KW - Parameter tuning
UR - http://www.scopus.com/inward/record.url?scp=85124161316&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124161316&partnerID=8YFLogxK
U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00138
DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00138
M3 - Conference contribution
AN - SCOPUS:85124161316
T3 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
SP - 984
EP - 992
BT - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Y2 - 30 September 2021 through 3 October 2021
ER -