Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach
arXiv:1103.4887 · doi:10.1016/j.jcp.2011.07.015
Abstract
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.
5 figures, submitted to Journal of Computational Physics
References in corpus (2)
Cited by in corpus (4)
- A self-organizing state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurons
- An optimization-based approach to calculating neutrino flavor evolution
- A path-integral approach to Bayesian inference for inverse problems using the semiclassical approximation
- GPU acceleration of ab initio simulations of large-scale identical particles based on path integral molecular dynamics