SPPAM: Signature Pattern Prediction and Access-Map Prefetcher

TL;DR

SPPAM is a novel cache prefetching technique that combines pattern learning and speculative lookahead to improve memory performance, addressing limitations of previous methods like SPP and AMPM.

Contribution

It introduces an online-learning based prefetcher that builds access-map patterns and uses confidence metrics for speculation, outperforming existing prefetchers.

Findings

Improves system performance by 31.4% over no prefetching.

Achieves 6.2% higher performance than Berti and Pythia.

Effectively combines pattern learning with speculative lookahead.

Abstract

The discrepancy between processor speed and memory system performance continues to limit the performance of many workloads. To address the issue, one effective and well studied technique is cache prefetching. Many prefetching designs have been proposed, with varying approaches and effectiveness. For example, SPP is a popular prefetcher that leverages confidence throttled recursion to speculate on the future path of program's references, however it is very susceptible to the reference reordering of higher-level caches and the out-of-order core. Orthogonally, AMPM is another popular approach to prefetching which uses reordering-resistant access maps to identify patterns within a region, but is unable to speculate beyond that region. In this paper, we propose SPPAM, a new approach to prefetching, inspired by prior works such as SPP and AMPM, while addressing their limitations. SPPAM utilizes online-learning to build a set of access-map patterns. These patterns are used in a speculative lookahead which is throttled by a confidence metric. Targeting the second-level cache, SPPAM alongside state-of-the-art prefetchers Berti and Bingo improves system performance by 31.4% over no prefetching and 6.2% over the baseline of Berti and Pythia.