| dc.contributor.author | Fett, Johannes | |
| dc.contributor.author | Schwarz, Christian | |
| dc.contributor.author | Kober, Urs | |
| dc.contributor.author | Habich, Dirk | |
| dc.contributor.author | Lehner, Wolfgang | |
| dc.contributor.editor | König-Ries, Birgitta | |
| dc.contributor.editor | Scherzinger, Stefanie | |
| dc.contributor.editor | Lehner, Wolfgang | |
| dc.contributor.editor | Vossen, Gottfried | |
| dc.date.accessioned | 2023-02-23T14:00:04Z | |
| dc.date.available | 2023-02-23T14:00:04Z | |
| dc.date.issued | 2023 | |
| dc.identifier.isbn | 978-3-88579-725-8 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/40356 | |
| dc.description.abstract | In this paper we introduce BEAM as a novel approach to perform GPU based matrix multiplication on compressed elements. BEAM allows flexible handling of bit sizes for both input and output elements. First evaluations show promising speedups compared to an uncompressed state-of-the-art matrix multiplication algorithm provided by nvidia. | en |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BTW 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-331 | |
| dc.subject | GPU | |
| dc.subject | Matrix multiplication | |
| dc.title | Improving GPU Matrix Multiplication by Leveraging Bit Level Granularity and Compression | en |
| dc.type | Text/Conference Paper | |
| dc.identifier.doi | 10.18420/BTW2023-49 | |
| gi.conference.location | Dresden, Germany | |
| gi.conference.date | 06.-10. März 2023 | |
| gi.citation.startPage | 763 | |
| gi.citation.endPage | 772 | |
| gi.citation.publisherPlace | Bonn | |
