Acceleration of discrete stochastic biochemical simulation using GPGPU

Frontiers in Physiology
Kei SumiyoshiAkira Funahashi

Abstract

For systems made up of a small number of molecules, such as a biochemical network in a single cell, a simulation requires a stochastic approach, instead of a deterministic approach. The stochastic simulation algorithm (SSA) simulates the stochastic behavior of a spatially homogeneous system. Since stochastic approaches produce different results each time they are used, multiple runs are required in order to obtain statistical results; this results in a large computational cost. We have implemented a parallel method for using SSA to simulate a stochastic model; the method uses a graphics processing unit (GPU), which enables multiple realizations at the same time, and thus reduces the computational time and cost. During the simulation, for the purpose of analysis, each time course is recorded at each time step. A straightforward implementation of this method on a GPU is about 16 times faster than a sequential simulation on a CPU with hybrid parallelization; each of the multiple simulations is run simultaneously, and the computational tasks within each simulation are parallelized. We also implemented an improvement to the memory access and reduced the memory footprint, in order to optimize the computations on the GPU. We also impl...Continue Reading

References

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Citations

Jul 13, 2016·Briefings in Bioinformatics·Marco S NobileDaniela Besozzi
Feb 11, 2016·Nihon yakurigaku zasshi. Folia pharmacologica Japonica·Akira Funahashi, Noriko Hiroi
Sep 10, 2021·PLoS Computational Biology·Andrea TangherloniDaniela Besozzi

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Software Mentioned

SSA
LibSBML
GPGPU
Systems Biology Markup Language ( SBML
CUDA SDK
CUDA

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