Abstract
Large-scale computational science simulations are a dominant component of the workload on modern supercomputers. Efficient use of high-end resources for these large computations is of considerable scientific and economic importance. However, conventional job schedulers limit flexibility in that they are ‘static’, i.e., the number of processors allocated to an application can not be changed at runtime. In earlier work, we described ReSHAPE, a system that eliminates this drawback by supporting dynamic resizability in distributed-memory parallel applications. The goal of this paper is to present a case study highlighting the steps involved in adapting a production scientific simulation code to take advantage of ReSHAPE. LAMMPS, a widely used molecular dynamics code, is the test case. Minor extensions to LAMMPS allow it to be resized using ReSHAPE, and experimental results show that resizing significantly improves overall system utilization as well as performance of an individual LAMMPS job.
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Sudarsan, R., Ribbens, C.: ReSHAPE: A Framework for Dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel Environment. In: Proceedings of the 2007 International Conference on Parallel Processing, Xian, China, pp. 44–54 (2007)
Sudarsan, R., Ribbens, C.: Scheduling Resizable Parallel Applications. In: Proceedings of 23rd IEEE International Parallel and Distributed Processing Symposium (to appear)
Sudarsan, R., Ribbens, C.: Efficient Multidimensional Data Redistribution for Resizable Parallel Computations. In: Proceedings of the International Symposium of Parallel and Distributed Processing and Applications, Niagara falls, ON, Canada, pp. 182–194 (2007)
Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. Journal of Computational Physics 117, 1–19 (1995)
Plimpton, S., Pollock, R., Stevens, M.: Particle-Mesh Ewald and rRESPA for Parallel Molecular Dynamics Simulations. In: Proceedings of the Eighth SIAM Conference on Parallel Processing for Scientific Computing, pp. 8–21 (1997)
Farkas, D., Mohanty, S., Monk, J.: Linear grain growth kinetics and rotation in nanocrystalline ni. Physical Review Letters 98, 165502 (2007)
Huedo, E., Montero, R., Llorente, I.: A Framework for Adaptive Execution in Grids. Software Practice and Experience 34, 631–651 (2004)
Buisson, J., Sonmez, O., Mohamed, H., Lammers, W., Epema, D.: Scheduling malleable applications in multicluster systems. In: Proceedings of the 2007 IEEE International Conference on Cluster Computing, Austin, USA, pp. 372–381 (2007)
Vadhiyar, S., Dongarra, J.: SRS - A framework for developing malleable and migratable parallel applications for distributed systems. Parallel Processing Letters 13, 291–312 (2003)
Moriera, J.E., Naik, V.K.: Dynamic resource management on distributed systems using reconfigurable applications. IBM Journal of Research and Development 41, 303–330 (1997)
Cirne, W., Berman, F.: Using Moldability to Improve the Performance of Supercomputer Jobs. Journal of Parallel and Distributed Computing 62, 1571–1602 (2002)
Open MPI v1.3 (2009), http://www.open-mpi.org
Van der Wijngaart, R., Wong, P.: NAS Parallel Benchmarks Version 2.4. NASA Ames Research Center: NAS Technical Report NAS-02-007 (2002)
Daw, M.S., Baskes, M.I.: Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals. Phys. Rev. B 29, 6443–6453 (1984)
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Sudarsan, R., Ribbens, C.J., Farkas, D. (2009). Dynamic Resizing of Parallel Scientific Simulations: A Case Study Using LAMMPS. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2009. Lecture Notes in Computer Science, vol 5544. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01970-8_18
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DOI: https://doi.org/10.1007/978-3-642-01970-8_18
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