Presentation
SIGN IN TO VIEW THIS PRESENTATION Sign In
Guiding Application Users via Estimation of Computational Resources for Massively Parallel Chemistry Computations
SessionAI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
DescriptionWe develop several machine learning (ML)-based methods to estimate resources required for massively-parallel chemistry computations, e.g., coupled-cluster methods, to guide application users before they run expensive simulations on supercomputers. By estimating computational resources, our ML-based methods predict optimal runtime parameters (number of nodes, tile sizes, etc.). With these predictions, we answer users' questions such as i) what is the minimum execution time for a given problem size?, ii) what are the number of nodes and tiles sizes to achieve this minimum execution time?, and iii) how about a supercomputer for which the number of past application runs that an ML model can be trained by is limited? Our work offers several ML models trained by the simulations of a coupled-cluster method run on Frontier, Aurora and Perlmutter supercomputers. We devise two strategies based on active and generative learning. By inquiring about costs beforehand, users can save significant amount of expenses.