GPU accelerated biochemical network simulation.

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 b

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC3051321 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature