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Analysis of Schedule and Layout Tuning for Sparse Matrices with Compound Entries on GPUs

: Mueller-Roemer, Johannes; Stork, André; Fellner, Dieter W.

Volltext urn:nbn:de:0011-n-5864803 (723 KByte PDF)
MD5 Fingerprint: 854c114fddf37b50d10c45d546dd06c9
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Erstellt am: 24.9.2020

Computer graphics forum 39 (2020), Nr.6, S.133-143
ISSN: 0167-7055
ISSN: 1467-8659
European Commission EC
H2020; 768892; CloudiFacturing
Cloudification of Production Engineering for Predictive Digital Manufacturing
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IGD ()
Lead Topic: Digitized Work; Research Line: (Interactive) simulation (SIM); general purpose computation on graphics processing unit (GPGPU); parallel computing; matrix operations

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 5.5×. In comparison to cuSPARSE, we achieve speedups of up to 4.7×.