Abstract
In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of biological systems that exploits the remarkable memory bandwidth and computational capability of GPUs. cupSODA allows to efficiently execute in parallel large numbers of simulations, which are usually required to investigate the emergent dynamics of a given biological system under different conditions. cupSODA works by automatically deriving the system of ordinary differential equations from a reaction-based mechanistic model, defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm, LSODA. We show that cupSODA can achieve a \(86 \times \) speedup on GPUs with respect to equivalent executions of LSODA on the CPU.
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Notes
cupSODA automatically executes the conversion from the stochastic to the deterministic formulation of both reaction constants and initial molecular amounts, given that the volume of the modeled biological system is specified.
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Nobile, M.S., Cazzaniga, P., Besozzi, D. et al. GPU-accelerated simulations of mass-action kinetics models with cupSODA. J Supercomput 69, 17–24 (2014). https://doi.org/10.1007/s11227-014-1208-8
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DOI: https://doi.org/10.1007/s11227-014-1208-8