To enable performance improvements in a power-efficient manner, computer architects have been building CPUs that exploit greater amounts of thread-level parallelism. A key consideration in such CPUs is properly designing the on-chip cache hierarchy. Unfortunately, this can be hard to do, especially for CPUs with high core counts and large amounts of cache. The enormous design space formed by the combinatorial number of ways in which to organize the cache hierarchy makes it difficult to identify power-efficient configurations. Moreover, the problem is exacerbated by the slow speed of architectural simulation, which is the primary means for conducting such design space studies. A powerful tool that can help architects optimize CPU cache hierarchies is reuse distance (RD) analysis. Recent work has extended uniprocessor RD techniques-i.e., by introducing concurrent RD and private-stack RD profiling—to enable analysis of different types of caches in multicore CPUs. Once acquired, parallel locality profiles can predict the performance of numerous cache configurations, permitting highly efficient design space exploration. To date, existing work on multicore RD analysis has focused on developing the profiling techniques and assessing their accuracy. Unfortunately, there has been no work on using RD analysis to optimize CPU performance or power consumption. This article investigates applying multicore RD analysis to identify the most power efficient cache configurations for a multicore CPU. First, we develop analytical models that use the cache-miss counts from parallel locality profiles to estimate CPU performance and power consumption. Although future scalable CPUs will likely employ multithreaded (and even out-of-order) cores, our current study assumes single-threaded in-order cores to simplify the models, allowing us to focus on the cache hierarchy and our RD-based techniques. Second, to demonstrate the utility of our techniques, we apply our models to optimize a large-scale tiled CPU architecture with a two-level cache hierarchy. We show that the most power efficient configuration varies considerably across different benchmarks, and that our locality profiles provide deep insights into why certain configurations are power efficient. We also show that picking the best configuration can provide significant gains, as there is a 2.01x power efficiency spread across our tiled CPU design space. Finally, we validate the accuracy of our techniques using detailed simulation. Among several simulated configurations, our techniques can usually pick the most power efficient configuration, or one that is very close to the best. In addition, across all simulated configurations, we can predict power efficiency with 15.2% error.