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Towards data-flow parallelization for adaptive mesh refinement applications
dc.contributor.author | Sala Penadés, Kevin |
dc.contributor.author | Rico Carro, Alejandro |
dc.contributor.author | Beltran Querol, Vicenç |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2020-12-09T08:13:20Z |
dc.date.available | 2020-12-09T08:13:20Z |
dc.date.issued | 2020 |
dc.identifier.citation | Sala, K.; Rico, A.; Beltran, V. Towards data-flow parallelization for adaptive mesh refinement applications. 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. 314-325. ISBN 978-1-7281-6677-3. DOI 10.1109/CLUSTER49012.2020.00042. |
dc.identifier.isbn | 978-1-7281-6677-3 |
dc.identifier.uri | http://hdl.handle.net/2117/334094 |
dc.description.abstract | Adaptive Mesh Refinement (AMR) is a prevalent method used by distributed-memory simulation applications to adapt the accuracy of their solutions depending on the turbulent conditions in each of their domain regions. These applications are usually dynamic since their domain areas are refined or coarsened in various refinement stages during their execution. Thus, they periodically redistribute their workloads among processes to avoid load imbalance. Although the defacto standard for scientific computing in distributed environments is MPI, in recent years, pure MPI applications are being ported to hybrid ones, attempting to cope with modern multi-core systems. Recently, the Task-Aware MPI library was proposed to efficiently integrate MPI communications and tasking models, providing also the transparent management of communications issued by tasks. In this paper, we demonstrate the benefits of porting AMR applications to data-flow programming models leveraging that novel hybrid approach. We exploit most of the application parallelism by taskifying all stages, allowing their natural overlap. We employ these techniques on the miniAMR proxy application, which mimics the refinement, load balancing, communication, and computation patterns of general AMR applications. We evaluate how this approach reduces the time in its computation and communication phases while achieving better programmability than other conventional hybrid techniques. |
dc.description.sponsorship | This work has been supported by the European Union H2020 Programme through the DEEP-EST project (agreement No. 754304), the Spanish Government through the Severo Ochoa Program (SEV-2015-0493), the Spanish Ministry of Science and Innovation (PID2019-107255GB), and the Generalitat de Catalunya (2017-SGR-1414). |
dc.format.extent | 12 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject.lcsh | Application program interfaces (Computer software) |
dc.subject.lcsh | Resource allocation |
dc.subject.lcsh | Data flow computing |
dc.subject.other | Adaptive mesh refinement |
dc.subject.other | AMR |
dc.subject.other | MPI |
dc.subject.other | Tasks |
dc.subject.other | Data-flow |
dc.subject.other | OpenMP |
dc.subject.other | OmpSs-2 |
dc.subject.other | TAMPI |
dc.subject.other | miniAMR |
dc.title | Towards data-flow parallelization for adaptive mesh refinement applications |
dc.type | Conference report |
dc.subject.lemac | Interfícies de programació d'aplicacions (Programari) |
dc.subject.lemac | Assignació de recursos |
dc.identifier.doi | 10.1109/CLUSTER49012.2020.00042 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9229616 |
dc.rights.access | Open Access |
local.identifier.drac | 29971039 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107255GB-C22/ES/UPC-COMPUTACION DE ALTAS PRESTACIONES VIII/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/2017 SGR 1414 |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/754304/EU/DEEP/DEEP-EST |
local.citation.author | Sala, K.; Rico, A.; Beltran, V. |
local.citation.contributor | IEEE International Conference on Cluster Computing |
local.citation.publicationName | 2020 IEEE International Conference on Cluster Computing: 14–17 September 2020, Kobe, Japan: proceedings |
local.citation.startingPage | 314 |
local.citation.endingPage | 325 |