Fast molecular electrostatics algorithms on GPUs

David J. Hardy, John E. Stone, Kirby L. Vandivort, David Gohara, Christopher Rodrigues, Klaus Schulten

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter presents GPU kernels for calculating electrostatic potential maps, which are of practical importance to modeling biomolecules. Calculations on a structured grid containing a large amount of fine-grained data parallelism make this problem especially well suited to GPU computing and a worthwhile case study. The chapter discusses the effective use of the hardware memory subsystems, kernel loop optimizations, and approaches to regularize the computational work performed by the GPU, all of which are important techniques for achieving high performance. The GPU kernels discussed here form the basis for the high-performance electrostatics algorithms used in the popular software packages VMD and APBS. VMD (visual molecular dynamics) is a popular software system designed for displaying, animating, and analyzing large biomolecular systems. Due to its versatility and user-extensibility, VMD is also capable of displaying other large datasets, such as sequencing data, quantum chemistry data, and volumetric data. A motivation for using GPU acceleration in VMD is to make slow batch-mode jobs fast enough for interactive use, which can drastically improve the productivity of scientific investigations. Summing the electrostatic contributions from a collection of charged particles onto a grid of points is inherently data parallel when decomposed over the grid points. Optimized kernels have demonstrated single GPU speeds ranging from 20 to 100 times faster than a conventional CPU core. An important extension to the short-range cutoffkernel is to compute the particle-particle nonbonded force interactions- generally the most time-consuming part of each time step in a molecular dynamics simulation. The calculation involves the gradients of the electrostatic and Lennard-Jones potential energy functions. © 2011

Original languageEnglish (US)
Title of host publicationGPU Computing Gems Emerald Edition
PublisherElsevier Inc.
Pages43-58
Number of pages16
ISBN (Print)9780123849885
DOIs
StatePublished - 2011

ASJC Scopus subject areas

  • Computer Science(all)

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    Hardy, D. J., Stone, J. E., Vandivort, K. L., Gohara, D., Rodrigues, C., & Schulten, K. (2011). Fast molecular electrostatics algorithms on GPUs. In GPU Computing Gems Emerald Edition (pp. 43-58). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-384988-5.00004-8