Abstract
We recently proposed a new approach to parallelization, by decomposing the time domain, instead of the conventional space domain. This improves latency tolerance, and we demonstrated its effectiveness in a practical application, where it scaled to much larger numbers of processors than conventional parallelization. This approach is fundamentally based on dynamically predicting the state of a system from data of related simulations. In earlier work, we used knowledge of the science of the problem to perform the prediction. In complicated simulations, this is not feasible. In this work, we show how reduced order modeling can be used for prediction, without requiring much knowledge of the science. We demonstrate its effectiveness in an important nano-materials application. The significance of this work lies in proposing a novel approach, based on established mathematical theory, that permits effective parallelization of time. This has important applications in multi-scale simulations, especially in dealing with long time-scales.
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References
Baffico, L., Bernard, S., Maday, Y., Turinici, G., Zerah, G.: Parallel-in-time molecular-dynamics simulations. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 66, 57701–57704 (2002)
Burkardt, J., Du, Q., Gunzburger, M., Lee, H.: Reduced order modeling of complex systems. In: Griffiths, D.F., Watson, G.A. (eds.) Proceedings of the 20 th biennial conference on Numerical Analysis, Dundee, Scotland, U.K, pp. 29–38. University of Dundee (2003)
Theory and modeling in nanoscience, 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, Department of Energy (May 2002)
Kolhe, J., Chandra, U., Namilae, S., Srinivasan, A., Chandra, N.: Parallel simulation of Carbon nanotube based composites. In: Bougé, L., Prasanna, V.K. (eds.) HiPC 2004. LNCS, vol. 3296, pp. 211–221. Springer, Heidelberg (2004)
Srinivasan, A., Chandra, N.: Latency tolerance through parallelization of time in scientific applications. Parallel Computing 31, 777–796 (2005)
Srinivasan, A., Yu, Y., Chandra, N.: Scalable parallelization of molecular dynamics simulations in nano mechanics, through time parallelization. Technical Report TR-050426, Department of Computer Science, Florida State University (2005)
Yakobson, B.I., Campbell, M.P., Brabec, C.J.: High strain rate fracture and C-chain unraveling in Carbon nanotubes. Computational Materials Science 8, 341–348 (1997)
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Srinivasan, A., Yu, Y., Chandra, N. (2005). Application of Reduce Order Modeling to Time Parallelization. In: Bader, D.A., Parashar, M., Sridhar, V., Prasanna, V.K. (eds) High Performance Computing – HiPC 2005. HiPC 2005. Lecture Notes in Computer Science, vol 3769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11602569_15
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DOI: https://doi.org/10.1007/11602569_15
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