AdaTP: Enhancing Temporal Prefetching with Adaptive Metadata Filtering
Junliang Wu, Feng Xue, Fuxin Zhang · 2025
Temporal prefetching is a promising technology to predict the memory addresses of irregular memory accesses.It retains correlations of cache miss addresses in metadata, which can be stored either on-chip or off-chip.Recent advancements have favoured on-chip metadata storage within portions of the last level cache, making the optimization of metadata storage effectiveness crucial, because the benefits brought by temporal prefetching can be easily offset by the reduced capacity for data in the last level cache.However, current state-of-the-art temporal prefetchers employ static strategies to filter the metadata, which often results in suboptimal performance gains.In this study, we introduce AdaTP, a novel method that dynamically adjusts metadata filtering strategy based on the runtime measurement of data and metadata demands.This adaptive filtering strategy leverages criticality and reuse conditions of load instructions.Specifically, it permits only the most critical loads with repetitive access patterns to store correlations when metadata storage is limited, and allows all loads to store correlations when sufficient storage is available.Our evaluations show that AdaTP achieves a 22.1% speedup compared to baseline stride prefetch in irregular memory intensive benchmarks in SPEC CPU2006 and SPEC CPU2017, and outperforms state-of-the-art temporal prefetcher Triage and Triangel by 4.0% and 6.5% respectively.