Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses
The application of sophisticated chemometrics techniques to large datasets has been made possible by continuing technological improvements in off-the-shelf computers. Recently, such improvements have been mainly achieved by the introduction of multi-core processors. However, the efficient use of multi-core hardware requires the development of software that properly address parallel computing. This paper is concerned with the implementation of parallelism using the Matlab Parallel Computing Toolbox, which requires only simple modifications to existing chemometrics code in order to exploit the benefits of multi-core processing. By using this software tool, it is shown that parallel implementations may provide substantial computational gains. In particular, the present study considers the problem of variable selection employing the successive projections algorithm and the genetic algorithm, as well as the use of cross-validation in partial least squares. For demonstration, two analytical applications are presented: determination of protein in wheat by near-infrared reflectance spectrometry and classification of edible vegetable oils by square-wave voltammetry. By using the proposed parallel computing implementations, computational gains of up to 204% were obtained.
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Sociedade Brasileira de Química
2010
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oai:scielo:S0103-505320100009000052010-09-10Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analysesSoares,Anderson da SilvaGalvão,Roberto K. HAraújo,Mário César USoares,Sófacles F. CPinto,Luiz Alberto parallel computation successive projections algorithm genetic algorithm partial least squares voltammetric analysis near-infrared spectrometric analysis The application of sophisticated chemometrics techniques to large datasets has been made possible by continuing technological improvements in off-the-shelf computers. Recently, such improvements have been mainly achieved by the introduction of multi-core processors. However, the efficient use of multi-core hardware requires the development of software that properly address parallel computing. This paper is concerned with the implementation of parallelism using the Matlab Parallel Computing Toolbox, which requires only simple modifications to existing chemometrics code in order to exploit the benefits of multi-core processing. By using this software tool, it is shown that parallel implementations may provide substantial computational gains. In particular, the present study considers the problem of variable selection employing the successive projections algorithm and the genetic algorithm, as well as the use of cross-validation in partial least squares. For demonstration, two analytical applications are presented: determination of protein in wheat by near-infrared reflectance spectrometry and classification of edible vegetable oils by square-wave voltammetry. By using the proposed parallel computing implementations, computational gains of up to 204% were obtained.info:eu-repo/semantics/openAccessSociedade Brasileira de QuímicaJournal of the Brazilian Chemical Society v.21 n.9 20102010-01-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532010000900005en10.1590/S0103-50532010000900005 |
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Soares,Anderson da Silva Galvão,Roberto K. H Araújo,Mário César U Soares,Sófacles F. C Pinto,Luiz Alberto |
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Soares,Anderson da Silva Galvão,Roberto K. H Araújo,Mário César U Soares,Sófacles F. C Pinto,Luiz Alberto Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses |
author_facet |
Soares,Anderson da Silva Galvão,Roberto K. H Araújo,Mário César U Soares,Sófacles F. C Pinto,Luiz Alberto |
author_sort |
Soares,Anderson da Silva |
title |
Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses |
title_short |
Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses |
title_full |
Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses |
title_fullStr |
Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses |
title_full_unstemmed |
Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses |
title_sort |
multi-core computation in chemometrics: case studies of voltammetric and nir spectrometric analyses |
description |
The application of sophisticated chemometrics techniques to large datasets has been made possible by continuing technological improvements in off-the-shelf computers. Recently, such improvements have been mainly achieved by the introduction of multi-core processors. However, the efficient use of multi-core hardware requires the development of software that properly address parallel computing. This paper is concerned with the implementation of parallelism using the Matlab Parallel Computing Toolbox, which requires only simple modifications to existing chemometrics code in order to exploit the benefits of multi-core processing. By using this software tool, it is shown that parallel implementations may provide substantial computational gains. In particular, the present study considers the problem of variable selection employing the successive projections algorithm and the genetic algorithm, as well as the use of cross-validation in partial least squares. For demonstration, two analytical applications are presented: determination of protein in wheat by near-infrared reflectance spectrometry and classification of edible vegetable oils by square-wave voltammetry. By using the proposed parallel computing implementations, computational gains of up to 204% were obtained. |
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Sociedade Brasileira de Química |
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2010 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532010000900005 |
work_keys_str_mv |
AT soaresandersondasilva multicorecomputationinchemometricscasestudiesofvoltammetricandnirspectrometricanalyses AT galvaorobertokh multicorecomputationinchemometricscasestudiesofvoltammetricandnirspectrometricanalyses AT araujomariocesaru multicorecomputationinchemometricscasestudiesofvoltammetricandnirspectrometricanalyses AT soaressofaclesfc multicorecomputationinchemometricscasestudiesofvoltammetricandnirspectrometricanalyses AT pintoluizalberto multicorecomputationinchemometricscasestudiesofvoltammetricandnirspectrometricanalyses |
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1756403404140183552 |