Accelerating protein structure recovery using graphics processing units

Authors:
Bryson R. Payne;G. Scott Owen;Irene Weber
Affiliations:
Department of ISCM, Georgia College & State University, Milledgeville, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Year:
2005

Citing 5
Cited 0

Computing in drug discovery: the design phase

Computing in Science and Engineering
The FFT on a GPU

Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Cg: a system for programming graphics hardware in a C-like language

ACM SIGGRAPH 2003 Papers
Brook for GPUs: stream computing on graphics hardware

ACM SIGGRAPH 2004 Papers
Accelerating scientific computation in bioinformatics by using graphics processing units as parallel vector processors

Accelerating scientific computation in bioinformatics by using graphics processing units as parallel vector processors

Quantified Score

Hi-index	0.00

Visualization

Abstract

Graphics processing units (GPUs) have evolved to become powerful, programmable vector processing units. Furthermore, the maximum processing power of current generation GPUs is technically superior to that of current generation CPUs (central processing units), and that power is doubling approximately every nine months, about twice the rate of Moore's law. This research represents the first successful application of GPU vector processing to an existing scientific computing software package, specifically an application for computing the tertiary (3D) geometric structures of protein molecules from x-ray crystallography data. A framework for applying GPU parallel processing to other computational tasks is developed and discussed, and an example of the benefits of taking advantage of the visualization potential of newer GPUs in scientific computing is presented.