GLUE Code: A framework handling communication and interfaces between scales
Aleksandra A. Pachalieva, Robert Pavel, Javier E. Santos, A. Diaw, Nicholas Lubbers, Mohamed Z. Mehana, J. Haack, Hari Viswanathan, Daniel Livescu, Timothy Clark Germann, Christoph Junghans · The Journal of Open Source Software · 2022
Many scientific applications are inherently multiscale in nature.Such complex physical phenomena often require simultaneous execution and coordination of simulations spanning multiple time and length scales.This is possible by combining expensive small-scale simulations (such as molecular dynamics simulations) with larger scale simulations (such continuum limit/hydro solvers) to allow for considerably larger systems using task and data parallelism.However, the granularity of the tasks can be very large and often leads to load imbalance.Traditionally, we use approximations to streamline the computation of the more costly interactions and this introduces trade-offs between simulation cost and accuracy.In recent years, the available computational power and the advances in machine learning have made computing these scale-bridging interactions and multiscale simulations more feasible.