CUDA Papers

http://impact.crhc.illinois.edu/ftp/conference/cf08.rodrigues.pdf

Abstract

The advent of systems biology requires the simulation of everlargerbiomolecular systems, demanding a commensurate growth incomputational power. This paper examines the use of the NVIDIATesla C870 graphics card programmed through the CUDA toolkitto accelerate the calculation of cutoff pair potentials, one of themost prevalent computations required by many different molecularmodeling applications. We present algorithms to calculate electrostaticpotential maps for cutoff pair potentials. Whereas a straightforwardapproach for decomposing atom data leads to low computeefciency, a newer strategy enables ne-grained spatial decompositionof atom data that maps efciently to the C870’s memorysystem while increasing work-efciency of atom data traversalby a factor of 5. The memory addressing exibility exposedthrough CUDA’s SPMD programming model is crucial in enablingthis new strategy. An implementation of the new algorithm providesa greater than threefold performance improvement over ourpreviously published implementation and runs 12 to 20 times fasterthan optimized CPU-only code. The lessons learned are generallyapplicable to algorithms accelerated by uniform grid spatial decomposition.

Authors

Christopher I. Rodrigues, Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign

David J. Hardy, John E. Stone†, Klaus Schulten†, Beckman Institute University of Illinois at Urbana-Champaign

Wen-Mei W. Hwu, Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign