Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach

TL;DR

This paper introduces a GPU-accelerated path integral Monte Carlo method for data assimilation, significantly speeding up computations by leveraging parallel processing on GPUs, demonstrated on a neuron model example.

Contribution

The paper presents a novel GPU-based parallel Monte Carlo approach for evaluating path integrals in data assimilation, achieving substantial speedup over traditional CPU methods.

Findings

Achieved up to 300x speedup using GPU computing.

Demonstrated effectiveness on Hodgkin-Huxley neuron model.

Performance improves with longer observation times.

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.