Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2013-06, Vol.8 (6), p.e66599-e66599
Main Authors: Wang, Wei-Jen, Hsieh, I-Fan, Chen, Chun-Chuan
Format: Article
Language:English
Subjects:
Algorithms
Biology
Biomedical engineering
Combinatorial analysis
Complexity
Computer Graphics
Computer programming
Computer Science
Computer Simulation
Computing time
Constraint modelling
Electroencephalography
Event-related potentials
Evoked Potentials
Experimental data
Graphics processing units
Hardware
Hypotheses
Iterative methods
Matlab
Medicine
Memory
Models, Statistical
Neural networks
Optimization
Parallel processing
Registration
Software
Therapeutic applications
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0066599