Custom Tailored Suite of Random Forests for Prefetcher Adaptation

TL;DR

This paper introduces SuitAP, a system that uses a suite of random forests to dynamically select and configure prefetchers at each memory level, significantly improving performance and reducing negative outliers.

Contribution

The paper presents a novel hardware prefetcher adapter that employs multiple random forests to optimize prefetcher configuration at runtime, addressing the lack of coordination among prefetchers.

Findings

Average IPC improvement of 46% across SPEC2017 traces

Reduces negative performance outliers

Operates with only 12KB overhead

Abstract

To close the gap between memory and processors, and in turn improve performance, there has been an abundance of work in the area of data/instruction prefetcher designs. Prefetchers are deployed in each level of the memory hierarchy, but typically, each prefetcher gets designed without comprehensively accounting for other prefetchers in the system. As a result, these individual prefetcher designs do not always complement each other, and that leads to low average performance gains and/or many negative outliers. In this work, we propose SuitAP (Suite of random forests for Adaptation of Prefetcher system configuration), which is a hardware prefetcher adapter that uses a suite of random forests to determine at runtime which prefetcher should be ON at each memory level, such that they complement each other. Compared to a design with no prefetchers, using SuitAP we improve IPC by 46% on average across traces generated from SPEC2017 suite with 12KB overhead. Moreover, we also reduce negative outliers using SuitAP.