Accurate address streams for LLC and beyond (SLAB): A methodology to enable system exploration

With increasing memory footprints and working set sizes of emerging workloads, system designers need to evaluate new memory hierarchies with large last level caches (LLCs), DRAM caches, large DRAMs, etc. to optimize performance gains. This requires a deep understanding of the memory access behavior of the target workloads. It is important to have accurate mechanisms to generate address streams to study memory access behavior at and beyond LLCs. Prior memory trace generation proposals such as WEST and STM utilize LRU stack distance to capture temporal locality in the data access streams. In addition, STM also captures spatial locality information by modeling stride-based access patterns. However, a key drawback of prior models is that the metadata that they store to capture locality is significantly high. In this paper, we propose an efficient, light-weight methodology to generate accurate traces for modeling address Streams for LLC And Beyond (SLAB). SLAB leverages the key insight that memory access patterns can be efficiently characterized by combining locality and reuse statistics captured from both instruction and address streams. Compared to prior studies, which capture patterns solely based on data addresses, using the additional instruction stream localized information significantly reduces the space complexity. For programs where dominant instruction-localized patterns do not exist, SLAB exploits multi-granularity data reuse distances. We evaluate SLAB using SPEC CPU2006 and Cloudsuite benchmarks. With meta-data sizes of less than 7% of the original LLC traces, SLAB demonstrates over 91% accuracy in replicating original application behavior across ~9000 different cache, prefetcher and memory configurations.