Spectral prefetcher

Abstract

Effective data prefetching requires accurate mechanisms to predict embedded patterns in the miss reference behavior. This paper proposes a novel prefetching mechanism, called the spectral prefetcher (SP), that accurately identifies the pattern by dynamically adjusting to its frequency. The proposed mechanism divides the memory address space into tag concentration zones (TCzones) and detects either the pattern of tags (higher order bits) or the pattern of strides (differences between consecutive tags) within each TCzone. The prefetcher dynamically determines whether the pattern of tags or strides will increase the effectiveness of prefetching and switches accordingly. To measure the performance of our scheme, we use a cycle-accurate aggressive out-of-order simulator that models bus occupancy, bus protocol, and limited bandwidth. Our experimental results show performance improvement of 1.59, on average, and at best 2.10 for the memory-intensive benchmarks we studied. Further, we show that SP outperforms the previously proposed scheme, with twice the size of SP, by 39% and a larger L2 cache, with equivalent storage area by 31%.