• Performance analysis of distri...

Treffer: Performance analysis of distributed GPU-accelerated task-based workflows

Title:
Performance analysis of distributed GPU-accelerated task-based workflows
Authors:
Nogueira Lobo de Carvalho, Marcos, Queralt Calafat, Anna, Romero Moral, Óscar, Simitsis, Alkis, Tatu, Cristian, Badia Sala, Rosa Maria
Contributors:
Universitat Politècnica de Catalunya. Doctorat en Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering
Publisher Information:
OpenProceedings
Publication Year:
2024
Collection:
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Subject Terms:
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, High performance computing, Graphics processing units, Machine learning, Supercomputadors, Unitats de processament gràfic, Aprenentatge automàtic
Document Type:
Konferenz conference object
File Description:
14 p.; application/pdf
Language:
English
Relation:
https://openproceedings.org/2024/conf/edbt/paper-161.pdf; info:eu-repo/grantAgreement/EC/H2020/955895/EU/Data Engineering for Data Science/DEDS; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117191RB-I00/ES/DESARROLLO, OPERATIVA Y GOBERNANZA DE DATOS PARA SISTEMAS SOFTWARE BASADOS EN APRENDIZAJE AUTOMATICO/; info:eu-repo/grantAgreement/EC/HE/101093164/EU/EXPeriment driven and user eXPerience oriented analytics for eXtremely Precise outcomes and decisions/ExtremeXP; info:eu-repo/grantAgreement/EC/HE/101092749/EU/Critical Action Planning over Extreme-Scale Data/CREXDATA; info:eu-repo/grantAgreement/EC/HE/101057264/EU/Core Components Supporting a FAIR EOSC/FAIRCORE4EOSC; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107255GB-C21/ES/BSC - COMPUTACION DE ALTAS PRESTACIONES VIII/; info:eu-repo/grantAgreement/AEI//CEX2021-001148-S; http://hdl.handle.net/2117/409326
DOI:
10.48786/edbt.2024.59
Availability:
http://hdl.handle.net/2117/409326
https://doi.org/10.48786/edbt.2024.59
Rights:
Attribution-NonCommercial-NoDerivatives 4.0 International ; http://creativecommons.org/licenses/by-nc-nd/4.0/ ; Open Access
Accession Number:
edsbas.6DC8F792
Database:
BASE

Weitere Informationen

We present an empirical approach to identify the key factors affecting the execution performance of task-based workflows on a High Performance Computing (HPC) infrastructure composed of heterogeneous CPU-GPU clusters. Our results reveal that the execution performance in distributed GPU-accelerated task-based workflows highly depends on several interrelated factors regarding the task algorithm, dataset, resources, and system employed. In addition, our analysis identifies key correlations among these factors, presents novel observations, and offers guidelines toward designing an automated method to handle task-based workflows in modern, high-compute capacity, CPU-GPU engines. ; This work has been partially supported by DEDS (H2020-MSCAITN2020) with grant agreement No. 955895, the EU-HORIZON programme CREXDATA under GA.101092749, the EU-HORIZON programme FAIR-CORE4EOSC under GA.101057264, the EUHORIZON programme EXTREMEXP under GA.101093164, the Spanish Government projects PID2019-107255GB and PID2020117191RB-I00/AEI/10.13039/501100011033andMCIN/AEI/10.13039 /501100011033 (CEX2021-001148-S), and by the Departament de Recerca i Universitats de la Generalitat de Catalunya (2021 SGR 00412, MPiEDist). ; Peer Reviewed ; Postprint (published version)