Mostrar el registro sencillo del ítem
Programming parallel dense matrix factorizations with look-ahead and OpenMP
dc.contributor.author | Catalán, Sandra | |
dc.contributor.author | Castelló, Adrián | |
dc.contributor.author | Igual, Francisco | |
dc.contributor.author | Rodríguez Sánchez, Rafael | |
dc.contributor.author | Quintana-Orti, Enrique S. | |
dc.date.accessioned | 2019-06-21T07:38:57Z | |
dc.date.available | 2019-06-21T07:38:57Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | CATALÁN, Sandra, et al. Programming parallel dense matrix factorizations with look-ahead and OpenMP. Cluster Computing, 2019 | ca_CA |
dc.identifier.issn | 1386-7857 | |
dc.identifier.issn | 1573-7543 | |
dc.identifier.uri | http://hdl.handle.net/10234/182890 | |
dc.description.abstract | We investigate a parallelization strategy for dense matrix factorization (DMF) algorithms, using OpenMP, that departs from the legacy (or conventional) solution, which simply extracts concurrency from a multi-threaded version of basic linear algebra subroutines (BLAS). The proposed approach is also different from the more sophisticated runtime-based implementations, which decompose the operation into tasks and identify dependencies via directives and runtime support. Instead, our strategy attains high performance by explicitly embedding a static look-ahead technique into the DMF code, in order to overcome the performance bottleneck of the panel factorization, and realizing the trailing update via a cache-aware multi-threaded implementation of the BLAS. Although the parallel algorithms are specified with a high level of abstraction, the actual implementation can be easily derived from them, paving the road to deriving a high performance implementation of a considerable fraction of linear algebra package (LAPACK) functionality on any multicore platform with an OpenMP-like runtime. | ca_CA |
dc.format.extent | 17 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation.isPartOf | Cluster Computing, 2019 | ca_CA |
dc.rights | © Springer Nature | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | matrix factorizations | ca_CA |
dc.subject | look-ahead | ca_CA |
dc.subject | multi-threading | ca_CA |
dc.subject | openMP | ca_CA |
dc.subject | lightweight threads | ca_CA |
dc.subject | high performance computing | ca_CA |
dc.title | Programming parallel dense matrix factorizations with look-ahead and OpenMP | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s10586-019-02927-z | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/article/10.1007/s10586-019-02927-z | ca_CA |
dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
ICC_Articles [427]