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Towards accelerating molecular modeling via multi-scale approximation on a GPU

M. Daga, Wu-chun Feng, T. Scogland
Dept. of Comput. Sci., Virginia Tech, Blacksburg, VA, USA
IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2011

@conference{daga2011towards,

   title={Towards accelerating molecular modeling via multi-scale approximation on a GPU},

   author={Daga, M. and Feng, W. and Scogland, T.},

   booktitle={Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on},

   pages={75–80},

   organization={IEEE}

}

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Research efforts to analyze biomolecular properties contribute towards our understanding of biomolecular function. Calculating non-bonded forces (or in our case, electrostatic surface potential) is often a large portion of the computational complexity in analyzing biomolecular properties. Therefore, reducing the computational complexity of these force calculations, either by improving the computational algorithm or by improving the underlying hardware on which the computational algorithm runs, can help to accelerate the discovery process. Traditional approaches seek to parallelize the electrostatic calculations to run on large-scale supercomputers, which are expensive and highly contended resources. Leveraging our multi-scale approximation algorithm for calculating electrostatic surface potential, we present a novel mapping and optimization of this algorithm on the graphics processing unit (GPU) of a desktop personal computer (PC). Our mapping and optimization of the algorithm results in a speed-up as high as four orders of magnitude, when compared to running serially on the same desktop PC, without deteriorating the accuracy of our results.
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