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. The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip . cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus ). Additional information about PySB is available at pysb.org. c.lopez@vanderbilt.edu ; paolo.cazzaniga@unibg.it. Supplementary data are available at Bioinformatics online.
%0 Journal Article
%1 Harris2017GPUpowered
%A Harris, Leonard A.
%A Nobile, Marco S.
%A Pino, James C.
%A Lubbock, Alexander L. R.
%A Besozzi, Daniela
%A Mauri, Giancarlo
%A Cazzaniga, Paolo
%A Lopez, Carlos F.
%D 2017
%J Bioinformatics (Oxford, England)
%K gpu ode python software
%T GPU-powered model analysis with PySB/cupSODA.
%U http://view.ncbi.nlm.nih.gov/pubmed/28666314
%X 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. The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip . cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus ). Additional information about PySB is available at pysb.org. c.lopez@vanderbilt.edu ; paolo.cazzaniga@unibg.it. Supplementary data are available at Bioinformatics online.
@article{Harris2017GPUpowered,
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. The {PySB}/{cupSODA} interface has been integrated into the {PySB} modeling framework (version 1.4.0), which can be installed from the Python Package Index ({PyPI}) using a Python package manager such as pip . {cupSODA} source code and precompiled binaries (Linux, Mac {OS}/X, Windows) are available at {github.com/aresio/cupSODA} (requires an Nvidia {GPU}; developer.nvidia.com/cuda-gpus ). Additional information about {PySB} is available at pysb.org. c.lopez@vanderbilt.edu ; paolo.cazzaniga@unibg.it. Supplementary data are available at Bioinformatics online.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Harris, Leonard A. and Nobile, Marco S. and Pino, James C. and Lubbock, Alexander L. R. and Besozzi, Daniela and Mauri, Giancarlo and Cazzaniga, Paolo and Lopez, Carlos F.},
biburl = {https://www.bibsonomy.org/bibtex/2645d3018d6ed904aa6c134ae0ee125a8/karthikraman},
citeulike-article-id = {14386468},
citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/28666314},
citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=28666314},
day = 28,
interhash = {a8095c78cc730522b60a22766bfee6cc},
intrahash = {645d3018d6ed904aa6c134ae0ee125a8},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {gpu ode python software},
month = jun,
pmid = {28666314},
posted-at = {2017-07-03 09:16:16},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {{GPU}-powered model analysis with {PySB}/{cupSODA}.},
url = {http://view.ncbi.nlm.nih.gov/pubmed/28666314},
year = 2017
}