Parallel Computing for Processing Data from Intelligent Transportation Systems

Jonathan Denis; Renzo Massobrio; Sergio Nesmachnow; Alfredo Cristóbal; Andrei Tchernykh; Esteban Meneses

doi:10.1007/978-3-030-38043-4_22

Parallel Computing for Processing Data from Intelligent Transportation Systems

Jonathan Denis
, Renzo Massobrio
, Sergio Nesmachnow
, Alfredo Cristóbal
, Andrei Tchernykh
, Esteban Meneses

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

This article describes the application of parallel computing techniques for efficiently processing large volumes of data from ITS. This is a relevant problem in nowadays societies, especially when working under the novel paradigm of smart cities. The proposed approach applies parallel multithreading computing for processing Global Positioning System records for a case study on the Intelligent Transportation System in Montevideo, Uruguay. The experimental analysis is performed on a high performance computing platform, considering a large volume of data and different computing resources. The main results indicate that the proposed approach allows achieving good speedup values, thus reducing the execution time to process more than 120 GB of data from 921 to 77 min, when using 32 threads. In addition, a web application to illustrate the results of the proposed approach for computing the average speed of public transportation in Montevideo, Uruguay, is described.

Idioma original	Inglés
Título de la publicación alojada	Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers
Editores	Moisés Torres, Jaime Klapp
Editorial	Springer
Páginas	266-281
Número de páginas	16
ISBN (versión impresa)	9783030380427
DOI	https://doi.org/10.1007/978-3-030-38043-4_22
Estado	Publicada - 2019
Evento	10th International Conference on Supercomputing, ISUM 2019 - Monterrey, México Duración: 25 mar 2019 → 29 mar 2019

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1151 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	10th International Conference on Supercomputing, ISUM 2019
País/Territorio	México
Ciudad	Monterrey
Período	25/03/19 → 29/03/19

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 11: Ciudades y comunidades sostenibles

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10.1007/978-3-030-38043-4_22

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Denis, J., Massobrio, R., Nesmachnow, S., Cristóbal, A., Tchernykh, A., & Meneses, E. (2019). Parallel Computing for Processing Data from Intelligent Transportation Systems. En M. Torres, & J. Klapp (Eds.), Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers (pp. 266-281). (Communications in Computer and Information Science; Vol. 1151 CCIS). Springer. https://doi.org/10.1007/978-3-030-38043-4_22

Denis, Jonathan ; Massobrio, Renzo ; Nesmachnow, Sergio et al. / Parallel Computing for Processing Data from Intelligent Transportation Systems. Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers. editor / Moisés Torres ; Jaime Klapp. Springer, 2019. pp. 266-281 (Communications in Computer and Information Science).

@inproceedings{e211462aecb74f6c8b55de358b4aef0c,

title = "Parallel Computing for Processing Data from Intelligent Transportation Systems",

abstract = "This article describes the application of parallel computing techniques for efficiently processing large volumes of data from ITS. This is a relevant problem in nowadays societies, especially when working under the novel paradigm of smart cities. The proposed approach applies parallel multithreading computing for processing Global Positioning System records for a case study on the Intelligent Transportation System in Montevideo, Uruguay. The experimental analysis is performed on a high performance computing platform, considering a large volume of data and different computing resources. The main results indicate that the proposed approach allows achieving good speedup values, thus reducing the execution time to process more than 120 GB of data from 921 to 77 min, when using 32 threads. In addition, a web application to illustrate the results of the proposed approach for computing the average speed of public transportation in Montevideo, Uruguay, is described.",

keywords = "Intelligent Transportation Systems, Parallel computing",

author = "Jonathan Denis and Renzo Massobrio and Sergio Nesmachnow and Alfredo Crist{\'o}bal and Andrei Tchernykh and Esteban Meneses",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 10th International Conference on Supercomputing, ISUM 2019 ; Conference date: 25-03-2019 Through 29-03-2019",

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doi = "10.1007/978-3-030-38043-4\_22",

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Denis, J, Massobrio, R, Nesmachnow, S, Cristóbal, A, Tchernykh, A & Meneses, E 2019, Parallel Computing for Processing Data from Intelligent Transportation Systems. En M Torres & J Klapp (eds.), Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1151 CCIS, Springer, pp. 266-281, 10th International Conference on Supercomputing, ISUM 2019, Monterrey, México, 25/03/19. https://doi.org/10.1007/978-3-030-38043-4_22

Parallel Computing for Processing Data from Intelligent Transportation Systems. / Denis, Jonathan; Massobrio, Renzo; Nesmachnow, Sergio et al.
Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers. ed. / Moisés Torres; Jaime Klapp. Springer, 2019. p. 266-281 (Communications in Computer and Information Science; Vol. 1151 CCIS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Parallel Computing for Processing Data from Intelligent Transportation Systems

AU - Denis, Jonathan

AU - Massobrio, Renzo

AU - Nesmachnow, Sergio

AU - Cristóbal, Alfredo

AU - Tchernykh, Andrei

AU - Meneses, Esteban

PY - 2019

Y1 - 2019

N2 - This article describes the application of parallel computing techniques for efficiently processing large volumes of data from ITS. This is a relevant problem in nowadays societies, especially when working under the novel paradigm of smart cities. The proposed approach applies parallel multithreading computing for processing Global Positioning System records for a case study on the Intelligent Transportation System in Montevideo, Uruguay. The experimental analysis is performed on a high performance computing platform, considering a large volume of data and different computing resources. The main results indicate that the proposed approach allows achieving good speedup values, thus reducing the execution time to process more than 120 GB of data from 921 to 77 min, when using 32 threads. In addition, a web application to illustrate the results of the proposed approach for computing the average speed of public transportation in Montevideo, Uruguay, is described.

AB - This article describes the application of parallel computing techniques for efficiently processing large volumes of data from ITS. This is a relevant problem in nowadays societies, especially when working under the novel paradigm of smart cities. The proposed approach applies parallel multithreading computing for processing Global Positioning System records for a case study on the Intelligent Transportation System in Montevideo, Uruguay. The experimental analysis is performed on a high performance computing platform, considering a large volume of data and different computing resources. The main results indicate that the proposed approach allows achieving good speedup values, thus reducing the execution time to process more than 120 GB of data from 921 to 77 min, when using 32 threads. In addition, a web application to illustrate the results of the proposed approach for computing the average speed of public transportation in Montevideo, Uruguay, is described.

KW - Intelligent Transportation Systems

KW - Parallel computing

UR - https://www.scopus.com/pages/publications/85077496286

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AN - SCOPUS:85077496286

SN - 9783030380427

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BT - Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers

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Denis J, Massobrio R, Nesmachnow S, Cristóbal A, Tchernykh A, Meneses E. Parallel Computing for Processing Data from Intelligent Transportation Systems. En Torres M, Klapp J, editores, Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers. Springer. 2019. p. 266-281. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-38043-4_22

Parallel Computing for Processing Data from Intelligent Transportation Systems

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