This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.Conventional parallelization strategies do not scale well when the computational effort arises from the need to simulate to long time spans, rather than from large state space. Molecular Dynamics simulations constitute an important class of applications where this proves to be a bottleneck. We are developing a new approach to parallelization of Molecular Dynamics, which is based on the observation that simulations typically occur in a context rich in data from other related simulations. We use such data to parallelize the time domain, which yields a more scalable algorithm. This approach is based on the observation that long time-spans are often encountered in simulations with multiple time scales. The fine scales are responsible for the large computational effort. However, the important contribution of the fine-scales is often to the effect they have on the coarse scale. We use reduced order modeling to identify important coarse scale effects. We use clustering and machine learning to dynamically determine the relationship between the simulation being performed and prior data. This enables us to predict reasonable initial states at future times, which can be used to parallelize the time domain.
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