SLAP-CC: Set-Level Adaptive Prefetching for Compressed Caches

Data prefetching and cache compression are well-studied techniques to reduce the impact of memory latency. Data prefetching predicts future memory accesses and prefills the cache with the corresponding memory blocks in advance of explicit demands. Cache compression tries to increase the effective capacity of the cache with minimum area overhead. As we show, however, naïvely integrating the two techniques does not yield additive gains. We show that the extra ways per set that cache compression provides generally produce fewer hits than the ways in the baseline uncompressed cache. Hence, prefetching more aggressively to those sets with compression-added ways and more conservatively to those sets with less compression provides substantial benefit. In this paper we present set-level adaptive prefetching for compressed caches (SLAP-CC), a compressibility aware prefetching technique that adapts prefetch aggressiveness to the workload compressibility to maximize prefetch coverage. SLAP-CC dynamically adjusts the prefetch confidence threshold on a per-set basis, based on the set’s compression. SLAP-CC achieves a geometric mean speedup of 18.0% over a baseline system with compressed cache and no prefetching. Further, SLAP-CC significantly outperforms existing state of the art L2 prefetchers, SPP [1] and Best Offset [2] when combined with cache compression by an average of 7% and up to 25% on some workloads.