Adaptive sparse matrix-matrix multiplication on the GPU

Martin Winter, Daniel Mlakar, Rhaleb Zayer, Hans-Peter Seidel, Markus Steinberger

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

In the ongoing efforts targeting the vectorization of linear algebra primitives, sparse matrix-matrix multiplication (SpGEMM) has received considerably less attention than sparse Matrix-Vector multiplication (SpMV). While both are equally important, this disparity can be attributed mainly to the additional formidable challenges raised by SpGEMM.
In this paper, we present a dynamic approach for addressing SpGEMM on the GPU. Our approach works directly on the standard compressed sparse rows (CSR) data format. In comparison to previous SpGEMM implementations, our approach guarantees a homogeneous, load-balanced access pattern to the first input matrix and improves memory access to the second input matrix. It adaptively re-purposes GPU threads during execution and maximizes the time efficient on-chip scratchpad memory can be used. Adhering to a completely deterministic scheduling pattern …
Originalspracheenglisch
TitelPPoPP '19, Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming
ErscheinungsortNew York, NY
Herausgeber (Verlag)Association of Computing Machinery
Seiten68-81
Seitenumfang14
ISBN (Print)978-1-4503-6225-2
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - Washington, DC, USA / Vereinigte Staaten
Dauer: 16 Feb 201920 Feb 2019

Konferenz

Konferenz24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
KurztitelPPoPP '19
Land/GebietUSA / Vereinigte Staaten
OrtWashington, DC
Zeitraum16/02/1920/02/19

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