Name
Abstract | ||
|---|---|---|
In this paper, we propose an auto-tuning (AT) system by adapting the A64 Scalable Vector Extension for SPIRAL to generate discrete Fourier transform (DFT) implementations. The performance of our method is evaluated using the Supercomputer "Flow" at Nagoya University. The A64 scalable vector extension applied DFT codes are up to 1.98 times faster than scalar DFT codes and up to 3.63 times higher in terms of the SIMD instruction rate. In addition, we obtain a factor of maximum speedup 2.32 by adapting proposed AT system for loop unrolling. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IPDPSW52791.2021.00117 | 2021 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW) |
Keywords | DocType | Citations |
Auto-tuning, Discrete Fourier Transform, SPIRAL, SIMD, A64 SVE | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naruya Kitai | 1 | 0 | 0.34 |
| Daisuke Takahashi | 2 | 297 | 39.92 |
| Franz Franchetti | 3 | 974 | 88.39 |
| Takahiro Katagiri | 4 | 0 | 0.34 |
| Satoshi Ohshima | 5 | 0 | 0.34 |
| Toru Nagai | 6 | 0 | 2.03 |