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Adapting a message-driven parallel application to GPU-accelerated clusters

James C. Phillips, John E. Stone, Klaus Schulten
Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
In SC ’08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008), pp. 1-9.

@conference{phillips2009adapting,

   title={Adapting a message-driven parallel application to GPU-accelerated clusters},

   author={Phillips, J.C. and Stone, J.E. and Schulten, K.},

   booktitle={High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference for},

   pages={1–9},

   year={2009},

   organization={IEEE}

}

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Graphics processing units (GPUs) have become an attractive option for accelerating scientific computations as a result of advances in the performance and flexibility of GPU hardware, and due to the availability of GPU software development tools targeting general purpose and scientific computation. However, effective use of GPUs in clusters presents a number of application development and system integration challenges. We describe strategies for the decomposition and scheduling of computation among CPU cores and GPUs, and techniques for overlapping communication and CPU computation with GPU kernel execution. We report the adaptation of these techniques to NAMD, a widely-used parallel molecular dynamics simulation package, and present performance results for a 64-core 64-GPU cluster.
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