Mueller-Roemer, Johannes ; Stork, André ; Fellner, Dieter W. (2019)
Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs.
24th International Symposium on Vision, Modeling, and Visualization. Rostock, Germany (Sep. 30. - Oct. 02., 2019)
doi: 10.2312/vmv.20191324
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Large sparse matrices with compound entries, i.e., complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation, and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5:5x. In comparison to cuSPARSE, we achieve speedups of up to 4:7x.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Mueller-Roemer, Johannes ; Stork, André ; Fellner, Dieter W. |
| Art des Eintrags: | Bibliographie |
| Titel: | Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Veranstaltungstitel: | 24th International Symposium on Vision, Modeling, and Visualization |
| Veranstaltungsort: | Rostock, Germany |
| Veranstaltungsdatum: | Sep. 30. - Oct. 02., 2019 |
| DOI: | 10.2312/vmv.20191324 |
| Kurzbeschreibung (Abstract): | Large sparse matrices with compound entries, i.e., complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation, and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5:5x. In comparison to cuSPARSE, we achieve speedups of up to 4:7x. |
| Freie Schlagworte: | General Purpose Computation on Graphics Processing Unit (GPGPU) GPU computing Linear systems Code generation |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 09 Apr 2020 12:55 |
| Letzte Änderung: | 04 Feb 2022 12:39 |
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