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dc.contributor.authorMaroñas Bravo, Marcos
dc.contributor.authorTeruel García, Xavier
dc.contributor.authorBull, J. Mark
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorBeltran Querol, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2020-11-18T15:05:04Z
dc.date.available2020-11-18T15:05:04Z
dc.date.issued2020
dc.identifier.citationMaroñas, M. [et al.]. Evaluating worksharing tasks on distributed environments. A: IEEE International Conference on Cluster Computing. "2020 IEEE International Conference on Cluster Computing: 14–17 September 2020, Kobe, Japan: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 69-80. ISBN 978-1-7281-6677-3. DOI 10.1109/CLUSTER49012.2020.00017.
dc.identifier.isbn978-1-7281-6677-3
dc.identifier.urihttp://hdl.handle.net/2117/332463
dc.description©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractHybrid programming is a promising approach to exploit clusters of multicore systems. Our focus is on the combination of MPI and tasking. This hybrid approach combines the low-latency and high throughput of MPI with the flexibility of tasking models and their inherent ability to handle load imbalance. However, combining tasking with standard MPI implementations can be a challenge. The Task-Aware MPI library (TAMPI) eases the development of applications combining tasking with MPI. TAMPI enables developers to overlap computation and communication phases by relying on the tasking data-flow execution model. Using this approach, the original computation that was distributed in many different MPI ranks is grouped together in fewer MPI ranks, and split into several tasks per rank. Nevertheless, programmers must be careful with task granularity. Too fine-grained tasks introduce too much overhead, while too coarse-grained tasks lead to lack of parallelism. An adequate granularity may not always exist, especially in distributed environments where the same amount of work is distributed among many more cores. Worksharing tasks are a special kind of tasks, recently proposed, that internally leverage worksharing techniques. By doing so, a single worksharing task may run in several cores concurrently. Nonetheless, the task management costs remain the same than a regular task. In this work, we study the combination of worksharing tasks and TAMPI on distributed environments using two well known mini-apps: HPCCG and LULESH. Our results show significant improvements using worksharing tasks compared to regular tasks, and to other state-of-the-art alternatives such as OpenMP worksharing.
dc.description.sponsorshipThis project is supported by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No.s 754304 (DEEP-EST) and 823767 (PRACE), the Ministry of Economy of Spain through the Severo Ochoa Center of Excellence Program (SEV-2015-0493), by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB) and by the Generalitat de Catalunya (2017-SGR1481). The work has been performed under the Project HPCEUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme; in particular, the author gratefully acknowledges the support of Dr Mark Bull (EPCC) and the computer resources and technical support provided by EPCC.
dc.format.extent12 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshMultiprocessors -- Programming
dc.subject.otherHybrid-Programming
dc.subject.otherMPI+X
dc.subject.otherTasking
dc.subject.otherData-Flow
dc.subject.otherPerformance-Analysis
dc.titleEvaluating worksharing tasks on distributed environments
dc.typeConference report
dc.subject.lemacMultiprocessadors -- Programació
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1109/CLUSTER49012.2020.00017
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/9229574
dc.rights.accessOpen Access
local.identifier.drac29826231
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/754304/EU/DEEP/DEEP-EST
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/823767/EU/PRACE 6th Implementation Phase Project/PRACE-6IP
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/SEV-2015-0493
local.citation.authorMaroñas, M.; Teruel, X.; Bull, J.; Ayguadé, E.; Beltran, V.
local.citation.contributorIEEE International Conference on Cluster Computing
local.citation.publicationName2020 IEEE International Conference on Cluster Computing: 14–17 September 2020, Kobe, Japan: proceedings
local.citation.startingPage69
local.citation.endingPage80


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