Accelerating electrostatic surface potential calculation with multi-scale approximation on graphics processing units

Ramu Anandakrishnan, Tom R. W. Scogland, Andrew T. Fenley, John C. Gordon, Wu-chun Feng, Alexey V. Onufriev
Department of Computer Science, Virginia Tech, 2050 Torgersen Hall (0106), Blacksburg, VA 24061, United States
Journal of Molecular Graphics and Modelling, Vol. 28, No. 8. (16 June 2010), pp. 904-910


   title={Accelerating Electrostatic Surface Potential Calculation with Multiscale Approximation on Graphics Processing Units},

   author={Anandakrishnan, R. and Scogland, T.R.W. and Fenley, A.T. and Gordon, J.C. and Feng, W. and Onufriev, A.},

   journal={Journal of Molecular Graphics and Modelling},





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Tools that compute and visualize biomolecular electrostatic surface potential have been used extensively for studying biomolecular function. However, determining the surface potential for large biomolecules on a typical desktop computer can take days or longer using currently available tools and methods. Two commonly used techniques to speed-up these types of electrostatic computations are approximations based on multi-scale coarse-graining and parallelization across multiple processors. This paper demonstrates that for the computation of electrostatic surface potential, these two techniques can be combined to deliver significantly greater speed-up than either one separately, something that is in general not always possible. Specifically, the electrostatic potential computation, using an analytical linearized Poisson–Boltzmann (ALPB) method, is approximated using the hierarchical charge partitioning (HCP) multi-scale method, and parallelized on an ATI Radeon 4870 graphical processing unit (GPU). The implementation delivers a combined 934-fold speed-up for a 476,040 atom viral capsid, compared to an equivalent non-parallel implementation on an Intel E6550 CPU without the approximation. This speed-up is significantly greater than the 42-fold speed-up for the HCP approximation alone or the 182-fold speed-up for the GPU alone.
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