CUDAICA: GPU optimization of infomax-ICA EEG analysis
In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing inte...
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paper:paper_16875265_v2012_n_p_Raimondo2023-06-08T16:26:40Z CUDAICA: GPU optimization of infomax-ICA EEG analysis Kamienkowski, Juan Esteban Sigman, Mariano Fernández Slezak, Diego Brain computing Computing time EEG analysis Function calls Independent signals Multiple channels On-line analysis Vector processors Video cards Brain computer interface Matrix algebra Independent component analysis algorithm article computer graphics computer program electroencephalography human Internet signal processing Algorithms Computer Graphics Electroencephalography Humans Internet Signal Processing, Computer-Assisted Software In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80 of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation. © Copyright 2012 Federico Raimondo et al. Fil:Kamienkowski, J.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Fernandez Slezak, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2012 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16875265_v2012_n_p_Raimondo http://hdl.handle.net/20.500.12110/paper_16875265_v2012_n_p_Raimondo |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Brain computing Computing time EEG analysis Function calls Independent signals Multiple channels On-line analysis Vector processors Video cards Brain computer interface Matrix algebra Independent component analysis algorithm article computer graphics computer program electroencephalography human Internet signal processing Algorithms Computer Graphics Electroencephalography Humans Internet Signal Processing, Computer-Assisted Software |
spellingShingle |
Brain computing Computing time EEG analysis Function calls Independent signals Multiple channels On-line analysis Vector processors Video cards Brain computer interface Matrix algebra Independent component analysis algorithm article computer graphics computer program electroencephalography human Internet signal processing Algorithms Computer Graphics Electroencephalography Humans Internet Signal Processing, Computer-Assisted Software Kamienkowski, Juan Esteban Sigman, Mariano Fernández Slezak, Diego CUDAICA: GPU optimization of infomax-ICA EEG analysis |
topic_facet |
Brain computing Computing time EEG analysis Function calls Independent signals Multiple channels On-line analysis Vector processors Video cards Brain computer interface Matrix algebra Independent component analysis algorithm article computer graphics computer program electroencephalography human Internet signal processing Algorithms Computer Graphics Electroencephalography Humans Internet Signal Processing, Computer-Assisted Software |
description |
In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80 of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation. © Copyright 2012 Federico Raimondo et al. |
author |
Kamienkowski, Juan Esteban Sigman, Mariano Fernández Slezak, Diego |
author_facet |
Kamienkowski, Juan Esteban Sigman, Mariano Fernández Slezak, Diego |
author_sort |
Kamienkowski, Juan Esteban |
title |
CUDAICA: GPU optimization of infomax-ICA EEG analysis |
title_short |
CUDAICA: GPU optimization of infomax-ICA EEG analysis |
title_full |
CUDAICA: GPU optimization of infomax-ICA EEG analysis |
title_fullStr |
CUDAICA: GPU optimization of infomax-ICA EEG analysis |
title_full_unstemmed |
CUDAICA: GPU optimization of infomax-ICA EEG analysis |
title_sort |
cudaica: gpu optimization of infomax-ica eeg analysis |
publishDate |
2012 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16875265_v2012_n_p_Raimondo http://hdl.handle.net/20.500.12110/paper_16875265_v2012_n_p_Raimondo |
work_keys_str_mv |
AT kamienkowskijuanesteban cudaicagpuoptimizationofinfomaxicaeeganalysis AT sigmanmariano cudaicagpuoptimizationofinfomaxicaeeganalysis AT fernandezslezakdiego cudaicagpuoptimizationofinfomaxicaeeganalysis |
_version_ |
1768541864996110336 |