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Artículo

A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

Tommasel, AntonelaIcon ; Godoy, Daniela LisIcon ; Zunino Suarez, Alejandro OctavioIcon ; Mateos Diaz, Cristian MaximilianoIcon
Fecha de publicación: 05/2017
Editorial: Springer London Ltd
Revista: Knowledge And Information Systems
ISSN: 0219-1377
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Computación

Resumen

Matrix computations are both fundamental and ubiquitous in computational science, and as a result, they are frequently used in numerous disciplines of scientific computing and engineering. Due to the high computational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear algebra software libraries.
Palabras clave: Distributed Computing , Feature Selection , Matrix Arithmetic Operation , Sparse Matrix
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Atribución-NoComercial-SinDerivadas 2.5 Argentina (CC BY-NC-ND 2.5 AR)
Identificadores
URI: http://hdl.handle.net/11336/58575
DOI: http://dx.doi.org/10.1007/s10115-016-0981-5
URL: https://link.springer.com/article/10.1007%2Fs10115-016-0981-5
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Articulos(ISISTAN)
Articulos de INSTITUTO SUPERIOR DE INGENIERIA DEL SOFTWARE
Citación
Tommasel, Antonela; Godoy, Daniela Lis; Zunino Suarez, Alejandro Octavio; Mateos Diaz, Cristian Maximiliano; A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection; Springer London Ltd; Knowledge And Information Systems; 51; 2; 5-2017; 459-497
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