Accelerating numerical solution of stochastic differential equations with CUDA

Published: 1 January 2010| Version 1 | DOI: 10.17632/pt9t8d4w44.1
Contributors:
M. Januszewski,
Marcin Kostur

Description

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract Numerical integration of stochastic differential equations is commonly used in many branches of science. In this paper we present how to accelerate this kind of numerical calculations with popular NVIDIA Graphics Processing Units using the CUDA programming environment. We address general aspects of numerical programming on stream processors and illustrate them by two examples: the noisy phase dynamics in a Josephson junction and the noisy Kuramoto model. In presented cases the measured speedu... Title of program: SDE Catalogue Id: AEFG_v1_0 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. Versions of this program held in the CPC repository in Mendeley Data AEFG_v1_0; SDE; 10.1016/j.cpc.2009.09.009

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