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Optimal Utilization of Heterogeneous Resources for Biomolecular Simulations

Scott S. Hampton, Sadaf R. Alam, Paul S. Crozier, Pratul K. Agarwal
Oak Ridge National Laboratory, Oak Ridge TN 37830
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (November 2010), SC ’10, pp. 1-11

@conference{hampton2010optimal,

   title={Optimal utilization of heterogeneous resources for biomolecular simulations},

   author={Hampton, S.S. and Alam, S.R. and Crozier, P.S. and Agarwal, P.K.},

   booktitle={Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis},

   pages={1–11},

   year={2010},

   organization={IEEE Computer Society}

}

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Biomolecular simulations have traditionally benefited from increases in the processor clock speed and coarse-grain inter-node parallelism on large-scale clusters. With stagnating clock frequencies, the evolutionary path for performance of microprocessors is maintained by virtue of core multiplication. Graphical processing units (GPUs) offer revolutionary performance potential at the cost of increased programming complexity. Furthermore, it has been extremely challenging to effectively utilize heterogeneous resources (host processor and GPU cores) for scientific simulations, as underlying systems, programming models and tools are continually evolving. In this paper, we present a parametric study demonstrating approaches to exploit resources of heterogeneous systems to reduce time-to-solution of a production-level application for biological simulations. By overlapping and pipelining computation and communication, we observe up to 10-fold application acceleration in multi-core and multi-GPU environments illustrating significant performance improvements over code acceleration approaches, where the host-to-accelerator ratio is static, and is constrained by a given algorithmic implementation.
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