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GPU-powered model analysis with PySB/cupSODA
SUMMARY: 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 o...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860165/ https://www.ncbi.nlm.nih.gov/pubmed/28666314 http://dx.doi.org/10.1093/bioinformatics/btx420 |
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author | Harris, Leonard A Nobile, Marco S Pino, James C Lubbock, Alexander L R Besozzi, Daniela Mauri, Giancarlo Cazzaniga, Paolo Lopez, Carlos F |
author_facet | Harris, Leonard A Nobile, Marco S Pino, James C Lubbock, Alexander L R Besozzi, Daniela Mauri, Giancarlo Cazzaniga, Paolo Lopez, Carlos F |
author_sort | Harris, Leonard A |
collection | PubMed |
description | SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5860165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58601652018-03-21 GPU-powered model analysis with PySB/cupSODA Harris, Leonard A Nobile, Marco S Pino, James C Lubbock, Alexander L R Besozzi, Daniela Mauri, Giancarlo Cazzaniga, Paolo Lopez, Carlos F Bioinformatics Applications Notes SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-11-01 2017-06-28 /pmc/articles/PMC5860165/ /pubmed/28666314 http://dx.doi.org/10.1093/bioinformatics/btx420 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Harris, Leonard A Nobile, Marco S Pino, James C Lubbock, Alexander L R Besozzi, Daniela Mauri, Giancarlo Cazzaniga, Paolo Lopez, Carlos F GPU-powered model analysis with PySB/cupSODA |
title | GPU-powered model analysis with PySB/cupSODA |
title_full | GPU-powered model analysis with PySB/cupSODA |
title_fullStr | GPU-powered model analysis with PySB/cupSODA |
title_full_unstemmed | GPU-powered model analysis with PySB/cupSODA |
title_short | GPU-powered model analysis with PySB/cupSODA |
title_sort | gpu-powered model analysis with pysb/cupsoda |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860165/ https://www.ncbi.nlm.nih.gov/pubmed/28666314 http://dx.doi.org/10.1093/bioinformatics/btx420 |
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