Custom Tailored Suite of Random Forests for Prefetcher Adaptation
This work addresses performance inefficiencies in computer systems for hardware designers, offering a domain-specific solution that is incremental in nature.
The paper tackled the problem of suboptimal performance from uncoordinated prefetchers in memory hierarchies by proposing SuitAP, a hardware adapter that uses a suite of random forests to dynamically configure prefetchers, resulting in a 46% average IPC improvement and reduced negative outliers on SPEC2017 traces with 12KB overhead.
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.