A Complexity-Effective Local Delta Prefetcher

Abstract

Data prefetching is crucial for performance in modern processors by effectively masking long-latency memory accesses. Over the past decades, numerous data prefetching mechanisms have been proposed, which have continuously reduced the access latency to the memory hierarchy. Several state-of-the-art prefetchers, namely Instruction Pointer Classifier Prefetcher (IPCP) and Berti, target the first-level data cache, and thus, they are able to completely hide the miss latency for timely prefetched cache lines. Berti exploits timely local deltas to achieve high accuracy and performance. This paper extends Berti with a larger evaluation and with extra optimizations on top of the previous conference paper. The result is a complexity-effective version of Berti that outperforms it for a large amount of workloads and simplifies its control logic. The key for those advancements is a simple mechanism for learning timely deltas without the need to track the fetch latency of each cache miss. Our experiments conducted with a wide range of workloads (CVP traces by Qualcomm, SPEC CPU2017, and GAP) show performance improvements by 4.0% over a mainstream stride prefetcher, and by a non-negligible 1.4% over the previously published version of Berti requiring similar storage.