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The role of storage target allocation in applications' I/O performance with BeeGFS

Authors :
Boito, Francieli
Pallez, Guillaume
Teylo, Luan
Topology-Aware System-Scale Data Management for High-Performance Computing (TADAAM)
Laboratoire Bordelais de Recherche en Informatique (LaBRI)
Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Plafrim
ANR-17-CE25-0004,DASH,Ordonnancement de données pour le calcul haute-performance(2017)
European Project: 956748,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT) ,ADMIRE(2021)
BOITO, Francieli Zanon
Ordonnancement de données pour le calcul haute-performance - - DASH2017 - ANR-17-CE25-0004 - AAPG2017 - VALID
Adaptive multi-tier intelligent data manager for Exascale - ADMIRE - - H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT) 2021-04-01 - 2024-03-31 - 956748 - VALID
Source :
CLUSTER 2022-IEEE International Conference on Cluster Computing, CLUSTER 2022-IEEE International Conference on Cluster Computing, Sep 2022, Heidelberg, Germany, 2022 IEEE International Conference on Cluster Computing (CLUSTER)
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

International audience; Parallel file systems are at the core of HPC I/O infrastructures. Those systems minimize the I/O time of applications by separating files into fixed-size chunks and distributing them across multiple storage targets. Therefore, the I/O performance experienced with a PFS is directly linked to the capacity to retrieve these chunks in parallel. In this work, we conduct an in-depth evaluation of the impact of the stripe count (the number of targets used for striping) on the write performance of BeeGFS, one of the most popular parallel file systems today. We consider different network configurations and show the fundamental role played by this parameter, in addition to the number of compute nodes, processes and storage targets. Through a rigorous experimental evaluation, we directly contradict conclusions from related work. Notably, we show that sharing I/O targets does not lead to performance degradation and that applications should use as many storage targets as possible. Our recommendations have the potential to significantly improve the overall write performance of BeeGFS deployments and also provide valuable information for future work on storage target allocation and stripe count tuning.

Details

Language :
English
ISBN :
978-1-66549-856-2
ISBNs :
9781665498562
Database :
OpenAIRE
Journal :
CLUSTER 2022-IEEE International Conference on Cluster Computing, CLUSTER 2022-IEEE International Conference on Cluster Computing, Sep 2022, Heidelberg, Germany, 2022 IEEE International Conference on Cluster Computing (CLUSTER)
Accession number :
edsair.doi.dedup.....f5439a244e3aca9a74aeb8020b3622c4