Multi-dimensional characterization of electrostatic surface potential computation on graphics processors

Multi-dimensional characterization of electrostatic surface potential computation on graphics processors

dc.contributor.department	Electrical and Computer Engineering	en
dc.contributor.department	Computer Science	en
dc.contributor.department	Fralin Life Sciences Institute	en
dc.date.accessioned	2012-04-12T12:47:22Z	en
dc.date.available	2012-04-12T12:47:22Z	en
dc.date.issued	2012-04-12	en
dc.date.updated	2012-04-12T12:47:22Z	en
dc.description.abstract	Background Calculating the electrostatic surface potential (ESP) of a biomolecule is critical towards understanding biomolecular function. Because of its quadratic computational complexity (as a function of the number of atoms in a molecule), there have been continual efforts to reduce its complexity either by improving the algorithm or the underlying hardware on which the calculations are performed. Results We present the combined effect of (i) a multi-scale approximation algorithm, known as hierarchical charge partitioning (HCP), when applied to the calculation of ESP and (ii) its mapping onto a graphics processing unit (GPU). To date, most molecular modeling algorithms perform an artificial partitioning of biomolecules into a grid/lattice on the GPU. In contrast, HCP takes advantage of the natural partitioning in biomolecules, which in turn, better facilitates its mapping onto the GPU. Specifically, we characterize the effect of known GPU optimization techniques like use of shared memory. In addition, we demonstrate how the cost of divergent branching on a GPU can be amortized across algorithms like HCP in order to deliver a massive performance boon. Conclusions We accelerated the calculation of ESP by 25-fold solely by parallelization on the GPU. Combining GPU and HCP, resulted in a speedup of at most 1,860-fold for our largest molecular structure. The baseline for these speedups is an implementation that has been hand-tuned SSE-optimized and parallelized across 16 cores on the CPU. The use of GPU does not deteriorate the accuracy of our results.	en
dc.description.version	Published version	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	BMC Bioinformatics. 2012 Apr 12;13(Suppl 5):S4	en
dc.identifier.doi	https://doi.org/10.1186/1471-2105-13-S5-S4	en
dc.identifier.uri	http://hdl.handle.net/10919/18653	en
dc.language.iso	en	en
dc.rights	Creative Commons Attribution 4.0 International	en
dc.rights.holder	et al.; licensee BioMed Central Ltd.	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.title	Multi-dimensional characterization of electrostatic surface potential computation on graphics processors	en
dc.title.serial	BMC Bioinformatics	en
dc.type	Article - Refereed	en
dc.type.dcmitype	Text	en