An Approach to Data Prefetching Using 2-Dimensional Selection Criteria

TL;DR

This paper introduces a data prefetching method that uses two-dimensional selection criteria based on instruction performance and usage patterns, enhancing prefetcher efficiency.

Contribution

It presents a novel approach combining pattern matching with dynamic selection criteria to improve data prefetching performance.

Findings

Performance improved by up to 9.5% over the previous best

Optimal prefetch degrees identified as 1, 4, and 8 lines

Performance variability observed with different configurations

Abstract

We propose an approach to data memory prefetching which augments the standard prefetch buffer with selection criteria based on performance and usage pattern of a given instruction. This approach is built on top of a pattern matching based prefetcher, specifically one which can choose between a stream, a stride, or a stream followed by a stride. We track the most recently called instructions to make a decision on the quantity of data to prefetch next. The decision is based on the frequency with which these instructions are called and the hit/miss rate of the prefetcher. In our approach, we separate the amount of data to prefetch into three categories: a high degree, a standard degree and a low degree. We ran tests on different values for the high prefetch degree, standard prefetch degree and low prefetch degree to determine that the most optimal combination was 1, 4, 8 lines respectively. The 2 dimensional selection criteria improved the performance of the prefetcher by up to 9.5% over the first data prefetching championship winner. Unfortunately performance also fell by as much as 14%, but remained similar on average across all of the benchmarks we tested.