Perceptron-Based Prefetch Filtering
Perceptron-Based Prefetch Filtering
复制标题
DOI:
10.1145/3307650.3322207
复制
发表时间:
2019-06
期刊:
影响因子:
--
通讯作者:
Eshan Bhatia;Gino Chacon;Seth H. Pugsley;Elvira Teran;Paul V. Gratz;Daniel A. Jiménez
中科院分区:
文献类型:
--
作者:
Eshan Bhatia;Gino Chacon;Seth H. Pugsley;Elvira Teran;Paul V. Gratz;Daniel A. Jiménez
Hardware prefetching is an effective technique for hiding cache miss latencies in modern processor designs. Prefetcher performance can be characterized by two main metrics that are generally at odds with one another: coverage, the fraction of baseline cache misses which the prefetcher brings into the cache; and accuracy, the fraction of prefetches which are ultimately used. An overly aggressive prefetcher may improve coverage at the cost of reduced accuracy. Thus, performance may be harmed by this over-aggressiveness because many resources are wasted, including cache capacity and bandwidth. An ideal prefetcher would have both high coverage and accuracy. In this paper, we introduce Perceptron-based Prefetch Filtering (PPF) as a way to increase the coverage of the prefetches generated by an underlying prefetcher without negatively impacting accuracy. PPF enables more aggressive tuning of the underlying prefetcher, leading to increased coverage by filtering out the growing numbers of inaccurate prefetches such an aggressive tuning implies. We also explore a range of features to use to train PPF's perceptron layer to identify inaccurate prefetches. PPF improves performance on a memory-intensive subset of the SPEC CPU 2017 benchmarks by 3.78% for a single-core configuration, and by 11.4% for a 4-core configuration, compared to the underlying prefetcher alone.