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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Supercomputing - 10th International Conference on Supercomputing in Mexico, ISUM 2019, Revised Selected Papers
Editors	Moisés Torres, Jaime Klapp
Publisher	Springer
Pages	266-281
Number of pages	16
ISBN (Print)	9783030380427
DOIs	https://doi.org/10.1007/978-3-030-38043-4_22
State	Published - 2019
Event	10th International Conference on Supercomputing, ISUM 2019 - Monterrey, Mexico Duration: 25 Mar 2019 → 29 Mar 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1151 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	10th International Conference on Supercomputing, ISUM 2019
Country/Territory	Mexico
City	Monterrey
Period	25/03/19 → 29/03/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Intelligent Transportation Systems
Parallel computing

Access to Document

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

Cite this

Denis, J., Massobrio, R., Nesmachnow, S., Cristóbal, A., Tchernykh, A., & Meneses, E. (2019). Parallel Computing for Processing Data from Intelligent Transportation Systems. In 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",

year = "2019",

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. in 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, Mexico, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

M3 - Contribución a la conferencia

AN - SCOPUS:85077496286

SN - 9783030380427

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

A2 - Torres, Moisés

A2 - Klapp, Jaime

PB - Springer

T2 - 10th International Conference on Supercomputing, ISUM 2019

Y2 - 25 March 2019 through 29 March 2019

ER -

Denis J, Massobrio R, Nesmachnow S, Cristóbal A, Tchernykh A, Meneses E. Parallel Computing for Processing Data from Intelligent Transportation Systems. In Torres M, Klapp J, editors, 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this