Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach

Autor:	Quinn, John C., Abarbanel, Henry D. I.
Rok vydání:	2011
Předmět:	Physics - Computational Physics Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
DOI:	10.1016/j.jcp.2011.07.015
Popis:	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. Comment: 5 figures, submitted to Journal of Computational Physics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1103.4887 Zobrazit plný text záznamu View this record from Arxiv