Conference paper
High-Performance Matrix-Vector Multiplication on the GPU
In
Lecture Notes in Computer Science
—
2012, pp. 377-386
Abstract
In this paper, we develop a high-performance GPU kernel for one of the most popular dense linear algebra operations, the matrix-vector multiplication. The target hardware is the most recent Nvidia Tesla 20-series (Fermi architecture), which is designed from the ground up for scientific computing. We show that it is essentially a matter of fully utilizing the fine-grained parallelism of the many-core GPU in order to achieve high-performance for dense matrix-vector multiplication.
We show that auto-tuning can be successfully employed to the GPU kernel so that it performs well for all matrix shapes and sizes.
| Language: | English |
|---|---|
| Publisher: | Springer |
| Year: | 2012 |
| Pages: | 377-386 |
| Proceedings: | Euro-Par 2011 |
| Series: | Lecture Notes in Computer Science |
| ISBN: | 3642297366 , 3642297374 , 9783642297366 and 9783642297373 |
| ISSN: | 03029743 |
| Types: | Conference paper |
| DOI: | 10.1007/978-3-642-29737-3_42 |
