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Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach

John C. Quinn, Henry D. I. Abarbanel
Department of Physics and BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093-0402 USA
arXiv:1103.4887 [physics.comp-ph] (25 Mar 2011)

@article{quinn2011data,

   title={Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach},

   author={Quinn, John C. and Abarbanel, Henry D. I.}

}

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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 200 times faster than an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.
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