title: STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB.
abstract: BACKGROUND
  The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that 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.
  
  
  BACKGROUND
  The software is open source under the GPL v3 and available at http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information.
  
  
  BACKGROUND
  klingbeil@maths.ox.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Centre for Mathematical Biology, Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford OX1 3LB, UK. klingbeil@maths.ox.ac.uk
authors.forename: Guido
authors.initials: G
authors.lastname: Klingbeil
authors.forename: Radek
authors.initials: R
authors.lastname: Erban
authors.forename: Mike
authors.initials: M
authors.lastname: Giles
authors.forename: Philip K
authors.initials: PK
authors.lastname: Maini
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D012984:Software; D013269:Stochastic Processes; D049490:Systems Biology / Q000379:methods*