Performance comparison of parallel fastICA algorithm in the PLGrid structures

Anna Gajos-Balinska,Grzegorz M Wojcik,Przemyslaw Stpiczynski,T Kwater,Z Gomolka,J Bartman,B Twarog

doi:10.1051/itmconf/20182100026

Anna Gajos-Balinska, Grzegorz M Wojcik + Show 5 more

Open Access

https://doi.org/10.1051/itmconf/20182100026

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Abstract

During processing the EEG signal, the methods of cleaning it from artifacts play an important role. One of the most commonly used methods is ICA (independent component analysis) [1-3]. However, algorithms of this type are computationally expensive. Known implementations of ICA type algorithms rarely include the possibility of parallel computing and do not use the capabilities provided by the architecture itself. This paper presents a parallel implementation of the fastICA algorithm using the available libraries and extensions of the Intel processors (such as BLAS, MKL, Cilk Plus) and compares the execution time for two selected architectures in the PLGrid structure (Zeus and Prometheus).

Highlights

Electroencephalography as a non-invasive diagnostic method has been used for many years both in medicine and in experimental psychology
It is common that one should use external software to take advantage of the aforementioned method. This prompted the authors of the research to write an application with its own implementation of independent component analysis (ICA), integrated with the software used in the Department of Neuroinformatics at Maria Curie-Sklodowska University in Lublin (NetStation) [8,9,10,11]
The obtained results showed that the execution time scales with the increase of the problem

Summary

Introduction

Electroencephalography as a non-invasive diagnostic method has been used for many years both in medicine and in experimental psychology. For this purpose, various methods of signal analysis and processing are applicable [4,5]. The recorded signal of brain activity is disturbed by external events, an important element of working with the EEG signal is to clean it from artifacts [6]. Another important element of signal analysis is the extraction of features for specific stimuli in ERP experiments (event-related potentials).

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