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HALO: A Hierarchical Memory Access Locality Modeling Technique For Memory System Explorations

Growing complexity of applications pose new challenges to memory system design due to their data intensive nature, complex access patterns, larger footprints, etc. The slow nature of full-system simulators, challenges of simulators to run deep software stacks of many emerging workloads, proprietary nature of software, etc. pose challenges to fast and accurate microarchitectural explorations of future memory hierarchies. One technique to mitigate this problem is to create spatio-temporal models of access streams and use them to explore memory system tradeoffs. However, existing memory stream models have weaknesses such as they only model temporal locality behavior or model spatio-temporal locality using global stride transitions, resulting in high storage/metadata overhead. In this paper, we propose HALO, a Hierarchical memory Access LOcality modeling technique that identifies patterns by isolating global memory references into localized streams and further zooming into each local stream capturing multi-granularity spatial locality patterns. HALO also models the interleaving degree between localized stream accesses leveraging coarse-grained reuse locality. We evaluate HALO's effectiveness in replicating original application performance using over 20K different memory system configurations and show that HALO achieves over 98.3%, 95.6%, 99.3% and 96% accuracy in replicating performance of prefetcher-enabled L1 & L2 caches, TLB and DRAM respectively. HALO outperforms the state-of-the-art memory cloning schemes, WEST and STM, while using ~39X less metadata storage than STM.