Abstract
Some of the most challenging applications to parallelize scalably are the ones that present a relatively small amount of computation per iteration. Multiple interacting performance challenges must be identified and solved to attain high parallel efficiency in such cases. We present a case study involving NAMD, a parallel molecular dynamics application, and efforts to scale it to run on 3000 processors with Tera-FLOPS level performance. NAMD is implemented in Charm++, and the performance analysis was carried out using “projections”, the performance visualization/analysis tool associated with Charm++. We will showcase a series of optimizations facilitated by projections. The resultant performance of NAMD led to a Gordon Bell award at SC2002.
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S. Adve, J. Mellor-Crummey, M. Anderson, K. Kennedy, J.-C. Wang, and D. A. Reed. An Integrated Compilation and Performance Analysis Environment for Data Parallel Programs. In Proceedings of Supercomputing’95, December 1995.
Milind Bhandarkar, L. V. Kale, Eric de Sturler, and Jay Hoeffinger. Object-Based Adaptive Load Balancing for MPI Programs. In Proceedings of the International Conference on Computational Science, San Francisco, CA, LNCS 2074, pages 108–117, May 2001.
M. T. Heath and J. A. Etheridge. Visualizing the Performance of Parallel Programs. IEEE Software, September 1991.
L. V. Kale and Sanjeev Krishnan. Charm++: Parallel Programming with Message-Driven Objects. In Gregory V. Wilson and Paul Lu, editors, Parallel Programming using C++, pages 175–213. MIT Press, 1996.
Laxmikant Kalé, Robert Skeel, Milind Bhandarkar, Robert Brunner, Attila Gursoy, Neal Krawetz, James Phillips, Aritomo Shinozaki, Krishnan Varadarajan, and Klaus Schulten. NAMD2: Greater scalability for parallel molecular dynamics. Journal of Computational Physics, 151:283–312, 1999.
O. Lawlor and L. V. Kalé. Supporting dynamic parallel object arrays. In Proceedings of ACM 2001 Java Grande/ISCOPE Conference, pages 21–29, Stanford, CA, Jun 2001.
James C. Phillips, Gengbin Zheng, Sameer Kumar, and Laxmikant V. Kalé. Namd: Biomolecular simulation on thousands of processors. In Proceedings of SC 2002, Baltimore, MD, September 2002.
Sanjeev Krishnan and L. V. Kale. A parallel array abstraction for data-driven objects. In Proceedings of Parallel Object-Oriented Methods and Applications Conference, Santa Fe, NM, February 1996.
Amitabh Sinha and L. V. Kale. Towards Automatic Peformance Analysis. In Proceedings of International Conference on Parallel Processing, volume III, pages 53–60, August 1996.
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© 2003 Springer-Verlag Berlin Heidelberg
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Kalé, L.V., Kumar, S., Zheng, G., Lee, C.W. (2003). Scaling Molecular Dynamics to 3000 Processors with Projections: A Performance Analysis Case Study. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44864-0_3
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DOI: https://doi.org/10.1007/3-540-44864-0_3
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