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Bioinformatics. 2011 Mar 15;27(6):874-6. doi: 10.1093/bioinformatics/btr015. Epub 2011 Jan 11.

GPU accelerated biochemical network simulation.

Author information

  • 1Institute of Mathematical Sciences, Imperial College London, London, UK.

Abstract

MOTIVATION:

Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code.

RESULTS:

We present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler-Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations.

AVAILABILITY:

http://cuda-sim.sourceforge.net/

PMID:
21224286
[PubMed - indexed for MEDLINE]
PMCID:
PMC3051321
Free PMC Article

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