Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach
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)
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.
March 28, 2011 by hgpu
