The trend for multicore processors is towards increasing numbers of cores, with 100s of cores-i.e. large-scale chip multiprocessors (LCMPs)-possible in the future. The key to realizing the potential of LCMPs is the cache hierarchy, so studying how memory performance will scale is crucial. Reuse distance (RD) analysis can help architects do this. In particular, recent work has developed concurrent reuse distance (CRD) and private reuse distance (PRD) profiles to enable analysis of shared and

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... ate caches. Also, techniques have been developed to predict profiles across problem size and core count, enabling the analysis of configurations that are too large to simulate. This paper applies RD analysis to study the scalability of multicore cache hierarchies. We present a framework based on CRD and PRD profiles for reasoning about the locality impact of core count and problem scaling. We find interference-based locality degradation is more significant than sharing-based locality degradation. For 256 cores running small problems, the former occurs at small cache sizes, allowing moderate capacity scaling of multicore caches to achieve the same cache performance (MPKI) as a single-core cache. At very large problems, interference-based locality degradation increases significantly in many of our benchmarks. For shared caches, this prevents most of our benchmarks from achieving constant-MPKI scaling within a 256 MB capacity budget; for private caches, all benchmarks cannot achieve constant-MPKI scaling within 256 MB.