ABSTRACT The importance of stochasticity in biological systems is becoming increasingly recognised and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU which exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM), and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user's models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. The software is open source under the GPL v3 and available at http://people.maths.ox.ac.uk/˜klingbeil/STOCHSIMGPU. The website also contains supplementary information.
StochSimGPU REF exploits GPGPU for parallel stochastic simulations of biological systems.
STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB
Bioinformatics
Bioinformatics
Computer Science Medicine