MSPA-MCS: Data-Driven Parallelization of Time in Molecular Dynamics Simulations

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Conventional parallelization strategies do not scalewell when the computational effort arises from theneed to simulate to long time spans, rather than fromlarge state space. Molecular Dynamics simulationsconstitute an important class of applications wherethis proves to be a bottleneck. The investigatorsdevelop a new approach to parallelization of MolecularDynamics, which is based on the observation thatsimulations typically occur in a context rich in datafrom other related simulations. They use such data toparallelize the time domain, which yields a morescalable algorithm. This approach is based on theobservation that long time-spans are often encounteredin simulations with multiple time scales. The finescales are responsible for the large computationaleffort. However, the important contribution of thefine-scales is often to the effect they have on thecoarse scale. The investigators use reduced ordermodeling to identify important coarse scale effects.They use clustering and machine learning todynamically determine the relationship between thesimulation being performed and prior data. They useODE and controls theory for stability analysis,uncertainty estimation, and system identification.The investigators validate their techniques using avariety of realistic applications in nano and bio-nanomaterials. The importance of the applications chosenarises from the fact that materials have historicallyplayed a pivotal role in human progress. An indicationof their importance lies in the fact that eras ofhuman progress, such as the iron age and the bronzeage, are named after the materials that contributed tosuch progress. Nano and bio-nano materials, designedbased on fundamental understanding at the atomicscale, promise yet another revolution, leading toproducts such as fuel efficient cars, disasterresistant structures, and new ways of treatingdiseases. The investigators' techniques remove animportant impediment to such developments.Furthermore, the students involved obtain training ininterdisciplinary research. Inclusion of acollaborator with a joint appointment at an HBCUensures that under-represented students too benefitfrom this work.