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

By: M Daga, Wu-chun Feng, and T Scogland

In: IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2011 pp. 75-80

Posted: 01 Jan 2011

Tagged: GPU GEM

This paper explores the benefits of Hierarchical Charge Partitioning with multiple levels as applied to a CUDA version of the GEM application originally explored on GPU in Accelerating electrostatic surface potential calculation with multi-scale approximation on graphics processing units. The final speedup over serial without HCP is completely staggering, at tens of thousands of times faster.


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.

    author = {Daga, M and Feng, Wu-chun and Scogland, T},
    title = {{Towards accelerating molecular modeling via multi-scale
    approximation on a GPU}},
    booktitle = {Computational Advances in Bio and Medical Sciences (ICCABS),
    2011 IEEE 1st International Conference on},
    year = {2011},
    pages = {75--80}

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