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dc.contributor.authorYu, Chenle
dc.contributor.authorRoyuela Alcázar, Sara
dc.contributor.authorQuiñones Moreno, Eduardo
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2021-09-16T08:30:12Z
dc.date.available2021-09-16T08:30:12Z
dc.date.issued2021
dc.identifier.citationYu, C.; Royuela, S.; Quiñones, E. Enhancing OpenMP tasking model: performance and portability. A: International Workshop on OpenMP. "OpenMP: Enabling Massive Node-Level Parallelism: 17th International Workshop on OpenMP, IWOMP 2021: Bristol, UK, September 14–16, 2021: proceedings". Berlín: Springer, 2021, p. 35-49. ISBN 978-3-030-85262-7. DOI 10.1007/978-3-030-85262-7_3.
dc.identifier.isbn978-3-030-85262-7
dc.identifier.urihttp://hdl.handle.net/2117/351422
dc.description.abstractOpenMP, as the de-facto standard programming model in symmetric multiprocessing for HPC, has seen its performance boosted continuously by the community, either through implementation enhancements or specification augmentations. Furthermore, the language has evolved from a prescriptive nature, as defined by the thread-centric model, to a descriptive behavior, as defined by the task-centric model. However, the overhead related to the orchestration of tasks is still relatively high. Applications exploiting very fine-grained parallelism and systems with a large number of cores available might fail on scaling. In this work, we propose to include the concept of Task Dependency Graph (TDG) in the specification by introducing a new clause, named taskgraph, attached to task or target directives. By design, the TDG allows alleviating the overhead associated with the OpenMP tasking model, and it also facilitates linking OpenMP with other programming models that support task parallelism. According to our experiments, a GCC implementation of the taskgraph is able to significantly reduce the execution time of fine-grained task applications and increase their scalability with regard to the number of threads.
dc.description.sponsorshipThis work has been supported by the EU H2020 project AMPERE under the grant agreement no. 871669.
dc.format.extent15 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures paral·leles
dc.subject.lcshSupercomputers
dc.subject.lcshParallel programming (Computer science)
dc.subject.lcshMultiprocessors
dc.subject.otherOpenMP specification
dc.subject.otherTasking model
dc.subject.otherRuntime overhead
dc.titleEnhancing OpenMP tasking model: performance and portability
dc.typeConference report
dc.subject.lemacSupercomputadors
dc.subject.lemacProgramació en paral·lel (Informàtica)
dc.subject.lemacMultiprocessadors
dc.identifier.doi10.1007/978-3-030-85262-7_3
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-85262-7_3
dc.rights.accessOpen Access
local.identifier.drac32029531
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/871669/EU/A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimisation/AMPERE
local.citation.authorYu, C.; Royuela, S.; Quiñones, E.
local.citation.contributorInternational Workshop on OpenMP
local.citation.pubplaceBerlín
local.citation.publicationNameOpenMP: Enabling Massive Node-Level Parallelism: 17th International Workshop on OpenMP, IWOMP 2021: Bristol, UK, September 14–16, 2021: proceedings
local.citation.startingPage35
local.citation.endingPage49


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