Data-Driven Time-Parallelization in the AFM Simulation of Proteins


Molecular dynamics is a popular technique to simulate the behavior of physical systems, with resolution at the atomic scale. One of its limitations is that an enormous computational effort is required to simulate to realistic time spans. Conventional parallelization strategies have limited effectiveness in dealing with this difficulty. We recently introduced a more scalable approach to parallelization, where data from prior, related, simulations are used to parallelize a simulation in the time domain. We demonstrated its effectiveness in nano-mechanics simulations. In this paper, we develop our approach so that it can be used in a soft-matter application involving the atomic force microscopy simulation of proteins. We obtain an order of magnitude improvement in performance when we combine time parallelization with conventional parallelization. The significance of this work lies in demonstrating the promise of data-driven time parallelization in soft-matter applications, which are more challenging than the hard-matter applications considered earlier.

DOI: 10.1109/IPDPS.2007.370450

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Computational science: Ensuring America's competitiveness Report of the President's Information Technology Advisory Committee

  • 2005
1 Excerpt

Report of the May 10-11, 2002 Workshop conducted by the basic energy sciences and advanced scientific computing advisory committees to the Office of Science

  • Theory, Nanoscience Modeling In
  • 2002
1 Excerpt