Abstract
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 …
| Originalsprache | englisch |
|---|---|
| Titel | PPoPP '19, Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming |
| Erscheinungsort | New York, NY |
| Herausgeber (Verlag) | Association of Computing Machinery |
| Seiten | 68-81 |
| Seitenumfang | 14 |
| ISBN (Print) | 978-1-4503-6225-2 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 2019 |
| Veranstaltung | 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - Washington, DC, USA / Vereinigte Staaten Dauer: 16 Feb 2019 → 20 Feb 2019 |
Konferenz
| Konferenz | 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming |
|---|---|
| Kurztitel | PPoPP '19 |
| Land | USA / Vereinigte Staaten |
| Ort | Washington, DC |
| Zeitraum | 16/02/19 → 20/02/19 |
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Dies zitieren
Adaptive sparse matrix-matrix multiplication on the GPU. / Winter, Martin; Mlakar, Daniel; Zayer, Rhaleb; Seidel, Hans-Peter; Steinberger, Markus.
PPoPP '19, Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming . New York, NY : Association of Computing Machinery, 2019. S. 68-81.Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Forschung › Begutachtung
}
TY - GEN
T1 - Adaptive sparse matrix-matrix multiplication on the GPU
AU - Winter, Martin
AU - Mlakar, Daniel
AU - Zayer, Rhaleb
AU - Seidel, Hans-Peter
AU - Steinberger, Markus
PY - 2019
Y1 - 2019
N2 - 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 …
AB - 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 …
U2 - 10.1145/3293883.3295701
DO - 10.1145/3293883.3295701
M3 - Conference contribution
SN - 978-1-4503-6225-2
SP - 68
EP - 81
BT - PPoPP '19, Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming
PB - Association of Computing Machinery
CY - New York, NY
ER -