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.

中文翻译：

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用于预取器适配的定制随机森林套件

为了缩小内存和处理器之间的差距，进而提高性能，在数据/指令预取器设计领域进行了大量工作。预取器部署在内存层次结构的每个级别中，但通常，每个预取器的设计都没有综合考虑系统中的其他预取器。因此，这些单独的预取器设计并不总是相互补充，这会导致低平均性能增益和/或许多负面异常值。在这项工作中，我们提出了 SuitAP（Suite of random forests for Adaptation of Prefetcher system configuration），这是一个硬件预取器适配器，它使用一套随机森林在运行时确定每个内存级别应该打开哪个预取器，这样它们相得益彰。与没有预取器的设计相比，使用 SuitAP，我们将 SPEC2017 套件生成的跟踪的 IPC 平均提高了 46%，开销为 12KB。此外，我们还使用 SuitAP 减少了负异常值。