Accurate and complexity-effective spatial pattern prediction

Recent research suggests that there are large variations in a cache's spatial usage, both within and across programs. Unfortunately, conventional caches typically employ fixed cache line sizes to balance the exploitation of spatial and temporal locality, and to avoid prohibitive cache fill bandwidth demands. The resulting inability of conventional caches to exploit spatial variations leads to suboptimal performance and unnecessary cache power dissipation. We describe the spatial pattern predictor (SPP), a cost-effective hardware mechanism that accurately predicts reference patterns within a spatial group (i.e., a contiguous region of data in memory) at runtime. The key observation enabling an accurate, yet low-cost, SPP design is that spatial patterns correlate well with instruction addresses and data reference offsets within a cache line. We require only a small amount of predictor memory to store the predicted patterns. Simulation results for a 64-Kbyte 2-way set-associative Ll data cache with 64-byte lines show that: (1) a 256-entry tag-less direct-mapped SPP can achieve, on average, a prediction coverage of 95%, over-predicting the patterns by only 8%, (2) assuming a 70 nm process technology, the SPP helps reduce leakage energy in the base cache by 41% on average, incurring less than 1% performance degradation, and (3) prefetching spatial groups of up to 512 bytes using SPP improves execution time by 33% on average and up to a factor of two.