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Scientific Programming
Volume 10 (2002), Issue 4, Pages 263-270
http://dx.doi.org/10.1155/2002/203525

Scalable Atomistic Simulation Algorithms for Materials Research

Aiichiro Nakano,1 Rajiv K. Kalia,1 Priya Vashishta,1 Timothy J. Campbell,2 Shuji Ogata,3 Fuyuki Shimojo,4 and Subhash Saini5

1Department of Computer Science, Department of Materials Science & Engineering, Department of Physics & Astronomy, Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA, and Concurrent Computing Laboratory for Materials Simulations, Biological Computation and Visualization Center, Department of Computer Science, Department, USA
2Naval Oceanographic Office, Stennis Space Center, MS 39529, USA
3Department of Applied Sciences, Yamaguchi University, Ube 755-8611, Japan
4Faculty of Integrated Arts and Sciences, Hiroshima University, Higashi-Hiroshima 739-0046, Japan
5IT Modeling and Simulation, NASA Ames Research Center, Moffett Field, CA 94035-1000, USA

Received 26 November 2002; Accepted 26 November 2002

Copyright © 2002 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A suite of scalable atomistic simulation programs has been developed for materials research based on space-time multiresolution algorithms. Design and analysis of parallel algorithms are presented for molecular dynamics (MD) simulations and quantum-mechanical (QM) calculations based on the density functional theory. Performance tests have been carried out on 1,088-processor Cray T3E and 1,280-processor IBM SP3 computers. The linear-scaling algorithms have enabled 6.44-billion-atom MD and 111,000-atom QM calculations on 1,024 SP3 processors with parallel efficiency well over 90%. production-quality programs also feature wavelet-based computational-space decomposition for adaptive load balancing, spacefilling-curve-based adaptive data compression with user-defined error bound for scalable I/O, and octree-based fast visibility culling for immersive and interactive visualization of massive simulation data.