A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.

(2017). GPU-powered model analysis with PySB/cupSODA [journal article - articolo]. In BIOINFORMATICS. Retrieved from http://hdl.handle.net/10446/106463

GPU-powered model analysis with PySB/cupSODA

MAURI, GIANCARLO;CAZZANIGA, Paolo;
2017-01-01

Abstract

A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.
journal article - articolo
2017
Harris, Leonard A; Nobile, Marco S; Pino, James C; Lubbock, Alexander L. R; Besozzi, Daniela; Mauri, Giancarlo; Cazzaniga, Paolo; Lopez, Carlos F....espandi
(2017). GPU-powered model analysis with PySB/cupSODA [journal article - articolo]. In BIOINFORMATICS. Retrieved from http://hdl.handle.net/10446/106463
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/106463
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