一句话：提出分层神经预取器 Voyager，可同时学习 delta 与地址相关性，并大幅降低开销。
论文针对现有神经预取器仅学习 delta 相关性、难以处理不规则访存的问题，提出 Voyager。其核心是分层结构，将地址分为页和页内偏移，并引入学习页间与偏移间重要关系的机制，从而同时捕捉 delta 相关性与地址相关性。在 SPEC 2006 与 GAP 的不规则程序上，Voyager 相对无预取系统平均提升 IPC 41.6%，高于理想化 Domino 的 21.7% 和 ISB 的 28.2%。在两个商业负载上，它大幅提升准确率和覆盖率。相比此前神经模型，其计算开销降低 15–20 倍、存储开销降低 110–200 倍，向实用神经预取器迈进一步。
This paper presents Voyager, a novel neural network for data prefetching. Unlike previous neural models for prefetching, which are limited to learning delta correlations, our model can also learn address correlations, which are important for prefetching irregular sequences of memory accesses. The key to our solution is its hierarchical structure that separates addresses into pages and offsets and that introduces a mechanism for learning important relations among pages and offsets. Voyager provides significant prediction benefits over current data prefetchers. For a set of irregular programs from the SPEC 2006 and GAP benchmark suites, Voyager sees an average IPC improvement of 41.6% over a system with no prefetcher, compared with 21.7% and 28.2%, respectively, for idealized Domino and ISB prefetchers. We also find that for two commercial workloads for which current data prefetchers see very little benefit, Voyager dramatically improves both accuracy and coverage. At present, slow training and prediction preclude neural models from being practically used in hardware, but Voyager’s overheads are significantly lower—in every dimension—than those of previous neural models. For example, computation cost is reduced by 15- 20×, and storage overhead is reduced by 110-200×. Thus, Voyager represents a significant step towards a practical neural prefetcher.
DOI 原文 ·
@inproceedings{paperbot6091,
title = {A hierarchical neural model of data prefetching},
author = {Zhan Shi and Akanksha Jain and Kevin Swersky and Milad Hashemi and Parthasarathy Ranganathan and Calvin Lin},
booktitle = {ASPLOS},
year = {2021},
doi = {10.1145/3445814.3446752}
}