Data assimilation using a GPU accelerated path integral Monte Carlo approach

JOURNAL OF COMPUTATIONAL PHYSICS

卷:230

Article

Quinn, John C.^1,2  Abarbanel, Henry D. I.^1,3,4 

[1] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA

[2] Univ Calif San Diego, BioCircuits Inst, La Jolla, CA 92093 USA

[3] Univ Calif San Diego, Marine Phys Lab, Scripps Inst Oceanog, La Jolla, CA 92093 USA

[4] Univ Calif San Diego, Ctr Theoret Biol Phys, La Jolla, CA 92093 USA

关键词: Data assimilation;    State and parameter estimation;    GPU computing;    Path integral Monte Carlo;    Hodgkin-Huxley;   

DOI  :  10.1016/j.jcp.2011.07.015

来源: Elsevier

【 摘 要 】

The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a graphics processing unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 300, compared to an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

Free