Centre for Mathematical Biology, Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford OX1 3LB, UK.
Bioinformatics. 2011 Apr 15;27(8):1170-1. doi: 10.1093/bioinformatics/btr068. Epub 2011 Feb 25.
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.
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://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information.
Supplementary data are available at Bioinformatics online.
生物系统中的随机性的重要性正日益得到认可，而生物现实随机模拟的计算成本迫切需要开发有效的软件。我们提出了一种新的软件工具 STOCHSIMGPU，它利用图形处理单元 (GPU) 对生物/化学反应系统进行并行随机模拟，并显示出可以显著提高效率。它集成在 MATLAB 中，并与 MATLAB 的系统生物学工具箱 2 (SBTOOLBOX2) 一起使用。
基于 GPU 的 Gillespie 随机模拟算法 (SSA)、对数直接方法 (LDM) 和下一个反应方法 (NRM) 的并行实现比在中央处理单元 (CPU) 上的 NRM 的顺序实现快约 85 倍。使用我们的软件不需要对用户的模型进行任何更改，因为它是 SBTOOLBOX2 的随机模拟软件的直接替代品。
该软件在 GPL v3 下开源，并可在 http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU 获得。该网站还包含补充信息。
补充数据可在Bioinformatics 在线获得。
