Name
Title | ||
|---|---|---|
Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse and Batched Computations |
Abstract | ||
|---|---|---|
GPU accelerators have become an Important backbone for scientific high performance-computing, and the performance advances obtained from adopting new GPU hardware are significant. In this paper we take a first look at NVIDIA's newest server-line GPU, the A100 architecture, part of the Ampere generation. Specifically, we assess its performance for sparse and batch computations, as these routines are relied upon in many scientific applications, and compare to the performance achieved on NVIDIA's previous server-line GPU. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/PMBS51919.2020.00009 | 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) |
Keywords | DocType | ISBN |
Sparse Linear Algebra,Sparse Matrix Vector Product,Batched Linear Algebra,NVIDIA A100 GPU | Conference | 978-1-6654-2266-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hartwig Anzt | 1 | 222 | 31.97 |
| Yuhsiang M. Tsai | 2 | 2 | 2.76 |
| Ahmad Abdelfattah | 3 | 88 | 12.71 |
| Terry Cojean | 4 | 9 | 4.27 |
| Jack J. Dongarra | 5 | 17625 | 2615.79 |