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Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs
| dc.contributor.author | Aliaga Estellés, José Ignacio | |
| dc.contributor.author | Anzt, Hartwig | |
| dc.contributor.author | Quintana-Orti, Enrique S. | |
| dc.contributor.author | Tomás Domínguez, Andrés Enrique | |
| dc.contributor.author | Tsai, Yuhsiang M. | |
| dc.date.accessioned | 2022-03-30T11:53:33Z | |
| dc.date.available | 2022-03-30T11:53:33Z | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | Aliaga, J.I., Anzt, H., Quintana-Ortí, E.S., Tomás, A.E., Tsai, Y.M. (2021). Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs. In: , et al. Euro-Par 2020: Parallel Processing Workshops. Euro-Par 2020. Lecture Notes in Computer Science(), vol 12480. Springer, Cham. https://doi.org/10.1007/978-3-030-71593-9_7 | ca_CA |
| dc.identifier.isbn | 978-3-030-71592-2 | |
| dc.identifier.isbn | 978-3-030-71593-9 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.uri | http://hdl.handle.net/10234/197137 | |
| dc.description.abstract | We contribute to the optimization of the sparse matrix-vector product on graphics processing units by introducing a variant of the coordinate sparse matrix layout that compresses the integer representation of the matrix indices. In addition, we employ a look-ahead table to avoid the storage of repeated numerical values in the sparse matrix, yielding a more compact data representation that is easier to maintain in the cache. Our evaluation on the two most recent generations of NVIDIA GPUs, the V100 and the A100 architectures, shows considerable performance improvements over the kernels for the sparse matrix-vector product in cuSPARSE (CUDA 11.0.167). | ca_CA |
| dc.format.extent | 13 p. | ca_CA |
| dc.format.mimetype | application/pdf | ca_CA |
| dc.language.iso | eng | ca_CA |
| dc.publisher | Springer | ca_CA |
| dc.relation.isPartOf | European Conference on Parallel Processing. Springer, Cham, 2020 | ca_CA |
| dc.rights | © Springer Nature Switzerland AG | ca_CA |
| dc.rights.uri | http://rightsstatements.org/vocab/CNE/1.0/ | ca_CA |
| dc.subject | sparse matrix-vector product | ca_CA |
| dc.subject | sparse matrix data layouts | ca_CA |
| dc.subject | sparse linear algebra | ca_CA |
| dc.subject | high performance computing | ca_CA |
| dc.subject | GPUs | ca_CA |
| dc.title | Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs | ca_CA |
| dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
| dc.identifier.doi | https://doi.org/10.1007/978-3-030-71593-9_7 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/732631 | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
| dc.relation.publisherVersion | https://link.springer.com/chapter/10.1007/978-3-030-71593-9_7 | ca_CA |
| dc.description.sponsorship | This work was partially sponsored by the EU H2020 project 732631 OPRECOMP and project TIN2017-82972-R of the Spanish MINECO. Hartwig Anzt and Yuhsiang M. Tsai were supported by the “Impuls und Vernetzungsfond” of the Helmholtz Association under grant VH-NG-1241 and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The authors would like to thank the Steinbuch Centre for Computing (SCC) of the Karlsruhe Institute of Technology for providing access to an NVIDIA A100 GPU. | |
| dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
| project.funder.name | Ministerio de Ciencia, Innovación y Universidades | ca_CA |
| project.funder.name | Helmholtz Association | ca_CA |
| project.funder.name | Exascale Computing Project | ca_CA |
| project.funder.name | European Commission | ca_CA |
| oaire.awardNumber | MICIU/ICTI2017-2020/TIN2017-82972-R | ca_CA |
| oaire.awardNumber | VH-NG-1241 | ca_CA |
| oaire.awardNumber | 17-SC-20-SC | ca_CA |
