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Manuscript Title: GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA
Authors: J. Spiechowicz, M. Kostur, L. Machura
Program title: poisson, dich
Catalogue identifier: AEVP_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 191(2015)140
Programming language: CUDA C.
Computer: Any with CUDA-compliant GPU.
Operating system: No limits (tested on Linux).
RAM: Hundreds of megabytes for typical case
Keywords: Stochastic differential equation, Langevin equation, graphics processing unit, GPGPU, NVIDIA, CUDA, numerical simulation, Monte Carlo method, Brownian motor, Gaussian noise, Poissonian noise, dichotomous noise.
PACS: 05.10.Gg, 05.40.-a, 05.40.Ca, 05.40.Jc, 05.60.Cd, 05.60.-k.
Classification: 4.3, 23.

External routines: The NVIDIA CUDA Random Number Generation library (cuRAND)

Nature of problem:
Graphics processing unit accelerated numerical simulation of stochastic differential equation.

Solution method:
The jump-adapted simplified weak order 2.0 predictor-corrector method is employed to integrate the Langevin equation of motion. Ensemble-averaged quantities of interest are obtained through averaging over multiple independent realizations of the system generated by means of the Monte Carlo method.

Unusual features:
The actual numerical simulation runs exclusively on the graphics processing unit using the CUDA environment. This allows for a speedup as large as about 3000 when compared to a typical CPU.

Running time:
A few seconds