Benchmarking and Implementation of Probability-Based Simulations on Programmable Graphics Cards
Brookhaven National Laboratory, Data Analysis and Visualization, Upton, NY
Computers & Graphics. Vol. 29, no. 1, pp. 71-80. Feb. 2005, arXiv:cs/0312006 [cs.GR] (2 Dec 2003)
@article{tomov2005benchmarking,
title={Benchmarking and implementation of probability-based simulations on programmable graphics cards},
author={Tomov, S. and McGuigan, M. and Bennett, R. and Smith, G. and Spiletic, J.},
journal={Computers & Graphics},
volume={29},
number={1},
pages={71–80},
issn={0097-8493},
year={2005},
publisher={Elsevier}
}
The latest Graphics Processing Units (GPUs) are reported to reach up to 200 billion floating point operations per second (200 Gflops) and to have price performance of 0.1 cents per M flop. These facts raise great interest in the plausibility of extending the GPUs’ use to non-graphics applications, in particular numerical simulations on structured grids (lattice). We review previous work on using GPUs for non-graphics applications, implement probability-based simulations on the GPU, namely the Ising and percolation models, implement vector operation benchmarks for the GPU, and finally compare the CPU’s and GPU’s performance. A general conclusion from the results obtained is that moving computations from the CPU to the GPU is feasible, yielding good time and price performance, for certain lattice computations. Preliminary results also show that it is feasible to use them in parallel.
November 13, 2010 by hgpu
