Abstract
A scalable parallel algorithm especially for large-scale three dimensional simulations with seriously non-uniform particles distributions is presented. In particular, based on cell-block data structures, this algorithm uses Hilbert space filling curve to convert three-dimensional domain decomposition for load distribution across processors into one-dimensional load balancing problems for which measurement-based multilevel averaging weights(MAW) method can be applied successfully. Against inverse space-filling partitioning(ISP), MAW redistributes blocks by monitoring change of total load in each processor. Numerical experimental results have shown that MAW is superior to ISP in rendering balanced load for large-scale multi-medium MD simulation in high temperature and high pressure physics. Excellent scalability was demonstrated, with a speedup larger than 200 with 240 processors of one MPP. The largest run with 1.1 × 109 particles on 500 processors took 80 seconds per time step.
Research supported by Chinese NSF( 60273030), Chinese 863 program(2002AA104570) and CAEP Funds.
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© 2004 Springer-Verlag Berlin Heidelberg
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Cao, X., Mo, Z. (2004). A New Scalable Parallel Method for Molecular Dynamics Based on Cell-Block Data Structure. In: Cao, J., Yang, L.T., Guo, M., Lau, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2004. Lecture Notes in Computer Science, vol 3358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30566-8_88
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DOI: https://doi.org/10.1007/978-3-540-30566-8_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24128-7
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