Computer Physics Communications Program LibraryPrograms in Physics & Physical Chemistry |

[Licence| Download | New Version Template] aefg_v1_0.tar.gz(5 Kbytes) | ||
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Manuscript Title: Accelerating numerical solution of Stochastic Differential Equations with CUDA. | ||

Authors: M. Januszewski, M. Kostur | ||

Program title: SDE | ||

Catalogue identifier: AEFG_v1_0Distribution format: tar.gz | ||

Journal reference: Comput. Phys. Commun. 181(2010)183 | ||

Programming language: CUDA C. | ||

Computer: any system with a CUDA-compatible GPU. | ||

Operating system: Linux. | ||

RAM: 64 MB of GPU memory | ||

Keywords: Josephson junction, Kuramoto, graphics processing unit, advanced computer architecture, numerical integration, diffusion, stochastic differential equation, CUDA, Tesla, NVIDIA. | ||

PACS: 05.40.Jc, 05.10.Gg, 85.25.Cp, 05.45.-a. | ||

Classification: 4.3. | ||

External routines: The program requires the NVIDIA CUDA Toolkit Version 2.0 or newer and the GNU Scientific Library v1.0 or newer. Optionally gnuplot is recommended for quick visulalisaion of the results. | ||

Nature of problem:Direct numerical integration of stochastic differential equations is a computationally intensive problem, due to the necessity of calculating multiple independent realizations of the system. We exploit the inherent parallelism of this problem and perform the calculations on GPUs using the CUDA programming environment. The GPU's ability to execute hundreds of threads simultaneously makes it possible to speed up the computation by over two orders of magnitude, compared to a typical modern CPU. | ||

Solution method:The stochastic Runge Kutta method of the second order is applied to integrate the equation of motion. Ensemble-averaged quantities of interest are obtained through averaging over multiple independent realizations of the system. | ||

Unusual features:The numerical solution of the stochastic differential equations in question is performed on a GPU using the CUDA environment. | ||

Running time:< 1 minute |

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