Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models
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Title: | Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models |
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Authors: | Cuenca, Javier | Cutillas-Lozano, José-Matías | Giménez, Domingo | Pérez-Bernabeu, Alberto | López-Espín, José J. |
Center, Department or Service: | Universidad de Alicante. Departamento de Fundamentos del Análisis Económico |
Keywords: | Computational econometric | VAR models | Parallelism | Metaheuristics | GPU |
Knowledge Area: | Fundamentos del Análisis Económico |
Issue Date: | 27-Oct-2020 |
Publisher: | MDPI |
Citation: | Cuenca J, Cutillas-Lozano J-M, Giménez D, Pérez-Bernabeu A, López-Espín JJ. Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models. Electronics. 2020; 9(11):1781. https://doi.org/10.3390/electronics9111781 |
Abstract: | In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to. |
Sponsor: | This work was supported by the Spanish MICINN and AEI, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53 and grant TIN2016-80565-R. |
URI: | http://hdl.handle.net/10045/110006 |
ISSN: | 2079-9292 |
DOI: | 10.3390/electronics9111781 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Peer Review: | si |
Publisher version: | https://doi.org/10.3390/electronics9111781 |
Appears in Collections: | Personal Investigador sin Adscripción a Grupo |
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