Authors: Harshita Sahni and Trilce Estrada (University of New Mexico)
Abstract: With modern technology and High-Performance Computing (HPC), Molecular Dynamics (MD) simulations can be task and data parallel. That means, they can be decomposed into multiple independent tasks (i.e., trajectories) with their own data, which can be processed in parallel. Analysis of MD simulations includes finding specific molecular events and the conformation changes that a protein undergoes. However, the traditional analysis relies on the global decomposition of all the trajectories for a specific molecular system, which can be performed only in a centralized way. We propose a lightweight self-supervised machine learning technique to analyze MD simulations in situ. That is, we aim to speed up the process of finding molecular events in the protein trajectory at run-time, without having to wait for the entire simulation to finish. This allows us to scale the analysis with the simulation.
Best Poster Finalist (BP): no
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