Fast heterogeneous computing with CUDA compatible Tesla GPU computing processor (personal supercomputing)

Authors:
D. Kumar;M. A. Qadeer
Affiliations:
Aligarh Muslim University, Aligarh, India;Aligarh Muslim University, Aligarh, India
Venue:
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Year:
2010

Citing 6
Cited 1

GPU Cluster for High Performance Computing

Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Green Supercomputing Comes of Age

IT Professional
General purpose molecular dynamics simulations fully implemented on graphics processing units

Journal of Computational Physics
GPU acceleration of cutoff pair potentials for molecular modeling applications

Proceedings of the 5th conference on Computing frontiers
Adapting a message-driven parallel application to GPU-accelerated clusters

Proceedings of the 2008 ACM/IEEE conference on Supercomputing
High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units

Bridging the GPGPU-FPGA efficiency gap

Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents how fast heterogeneous computing can be achieved with Tesla GPU computing processor. Tesla GPU super computer brings the performance of a cluster to a workstation and turning it into a supercomputer. We have chosen molecular dynamics field to show fast and high performance computing with Tesla GPU. We have given a DCS (direct coulomb summation) algorithm for computing electrostatic fields around molecules with CUDA. Tesla GPU speeds up the molecular dynamics application up to 240X. These Tesla GPUs can be programmed with a NVIDIA's multi core programming architecture CUDA that provides easy development environment and accelerate scientific and engineering applications up to great extent.