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. 2017 Nov 1;33(21):3492-3494.
doi: 10.1093/bioinformatics/btx420.

GPU-powered model analysis with PySB/cupSODA

Leonard A Harris  1   2 , Marco S Nobile  3   4 , James C Pino  2   5 , Alexander L R Lubbock  1   2 , Daniela Besozzi  3   4 , Giancarlo Mauri  3   4 , Paolo Cazzaniga  4   6 , Carlos F Lopez  1   2
Affiliations

GPU-powered model analysis with PySB/cupSODA

Leonard A Harris et al. Bioinformatics. .

Abstract

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.

Contact: paolo.cazzaniga@unibg.it or c.lopez@vanderbilt.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1
Fig. 1
(A–C) Run time comparisons between PySB/cupSODA and SciPy/LSODA for the example models in Table 1 (all simulations performed with the same initial protein concentrations and rate parameters). (D) Sensitivity in time-to-death in EARM to variations (±20%; 25 410 total simulations) in the initial protein concentrations (gold lines are medians; boxes range from the first to third quartile; whiskers extend to the minimum and maximum values). PySB/cupSODA simulations were run using cupSODA 1.0.0 on a GeForce GTX 980 Ti GPU (2816 cores, 16 threads/block); SciPy/LSODA simulations were run on an Intel Xeon E5-2667 v3 @ 3.20 GHz CPU (see Supplementary Table S1)
See this image and copyright information in PMC

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