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Mapping and scheduling HPC applications for optimizing I/O

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorCarretero Pérez, Jesús
dc.contributor.authorJeannot, Emmanuel
dc.contributor.authorPallez, Guillaume
dc.contributor.authorExpósito Singh, David
dc.contributor.authorVidal, Nicolas
dc.date.accessioned2022-01-14T10:26:53Z
dc.date.available2022-01-14T10:26:53Z
dc.date.issued2020-06-29
dc.description.abstractIn HPC platforms, concurrent applications are sharing the same file system. This can lead to conflicts, especially as applications are more and more data intensive. I/O contention can represent a performance bottleneck. The access to bandwidth can be split in two complementary yet distinct problems. The mapping problem and the scheduling problem. The mapping problem consists in selecting the set of applications that are in competition for the I/O resource. The scheduling problem consists then, given I/O requests on the same resource, in determining the order to these accesses to minimize the I/O time. In this work we propose to couple a novel bandwidth-aware mapping algorithm to I/O list-scheduling policies to develop a cross-layer optimization solution. We study this solution experimentally using an I/O middleware: CLARISSE. We show that naive policies such as FIFO perform relatively well in order to schedule I/O movements, and that the important part to reduce congestion lies mostly on the mapping part. We evaluate the algorithm that we propose using a simulator that we validated experimentally. This evaluation shows important gains for the simple, bandwidth-aware mapping solution that we provide compared to its non bandwidth-aware counterpart. The gains are both in terms of machine efficiency (makespan) and application efficiency (stretch). This stresses even more the importance of designing efficient, bandwidth-aware mapping strategies to alleviate the cost of I/O congestion.en
dc.description.sponsorshipThis work was supported in part by the French National Research Agency (ANR) in the frame of DASH (ANR-17-CE25-0004). Some of the experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Université de Bordeaux, Bordeaux INP and Conseil Régional d’Aquitaine (see https://www.plafrim.fr/).en
dc.identifier.bibliographicCitationJesus Carretero, Emmanuel Jeannot, Guillaume Pallez, David E. Singh, and Nicolas Vidal. 2020. Mapping and Scheduling HPC Applications for Optimizing I/O. In 2020 International Conference on Supercomputing (ICS ’20), June 29-July 2, 2020, Barcelona, Spain. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3392717.3392764en
dc.identifier.doihttps://doi.org/10.1145/3392717.3392764
dc.identifier.isbn978-1-4503-7983-0
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage12
dc.identifier.publicationtitleProceedings of the 34th ACM International Conference on Supercomputing (ICS-2020)en
dc.identifier.urihttps://hdl.handle.net/10016/33878
dc.identifier.uxxiCC/0000031366
dc.language.isoeng
dc.publisherAssociation For Computing Machinery (Acm)en
dc.relation.eventdate2020-06-29
dc.relation.eventplaceBARCELONAes
dc.relation.eventtitleICS '20: 34th ACM International Conference on Supercomputingen
dc.rights© 2020 Association for Computing Machineryen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherI/O schedulingen
dc.subject.otherI/O contentionen
dc.subject.othercross-layer optimizationsen
dc.subject.otherMPIen
dc.titleMapping and scheduling HPC applications for optimizing I/Oen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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