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Data assimilation using a GPU accelerated path integral Monte Carlo approach
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DOI：10.1016/j.jcp.2011.07.015.png)
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Abstract
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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.
Keywords:
Data assimilation
State and parameter estimation
GPU computing
Path integral Monte Carlo
Hodgkin-Huxley
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