Programs in Physics & Physical Chemistry
|[Licence| Download | New Version Template] aevp_v1_0.tar.gz(43 Kbytes)|
|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.
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.
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.
A few seconds
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