Name
Abstract | ||
|---|---|---|
Hardware prefetching involves a sophisticated balance between accuracy, coverage, and timeliness while minimizing hardware cost. Recent prefetchers have achieved these goals, but they still require complex hardware and a significant amount of storage. In this paper, we propose an efficient Per-page Most-Offset Prefetcher (PMOP) that minimizes hardware cost and simultaneously improves accuracy while maintaining coverage and timeliness. We achieve these objectives using an enhanced offset prefetcher that performs well with a reasonable hardware cost. Our approach first addresses coverage and timeliness by allowing multiple Most-Offset predictions. To minimize offset interference between pages, the PMOP leverages a fine-grain per-page offset filter. This filter records the access history with page-IDs, which enables efficient mapping and tracking of multiple offset streams from diverse pages. Analysis results show that PMOP outperforms the state-of-the-art Signature Path Prefetcher while reducing storage overhead by a factor of 3.4. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1587/transinf.2018EDP7328 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
memory hierarchy, cache, hardware prefetching | Computer vision,Computer science,Artificial intelligence,Offset (computer science) | Journal |
Volume | Issue | ISSN |
E102D | 7 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanghee Kim | 1 | 0 | 0.34 |
| Wooseok Lee | 2 | 13 | 2.09 |
| Sang-Bang Choi | 3 | 37 | 7.00 |