CUDA sources from the paper                              

"Accelerating numerical solution
of Stochastic Differential Equations with CUDA"

by Michał Januszewski and Marcin Kostur

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 speedup can be as high as 675x compared to a standard CPU, which corresponds to several billion integration steps per second. This means that calculations which took weeks can now be completed in less than one hour. This brings stochastic simulation to a completely new level, opening for research a whole new range of problems which can now be solved interactively. 

Links:

Paper on arxiv  (Comput. Phys. Commun. 181 (2010) 183: doi )

Source code(zip)

The source code requires CUDA compatible device installed on your computer  and nvcc compiler (available from nvidia).  E.g. graphics card Nvidia GeForce 8100 or better.

NEW:  easyy to use python version is available: sdepy.