Whereas clustered microarchitectures themselves have been extensively studied, the memory units for these clustered microarchitectures have received relatively little attention. This article discusses some of the inherent challenges of clustered memory units and shows how these can be overcome. Clustered memory pipelines work well with the late allocation of load/store queue entries and physically unordered queues. Yet this approach has characteristic problems such as queue overflows and allocation patterns that lead to deadlocks. We propose techniques to solve each of these problems and show that a distributed memory unit can offer significant energy savings and speedups over a centralized unit. For instance, compared to a centralized cache with a load/store queue of 64/24 entries, our four-cluster distributed memory unit with load/store queues of 16/8 entries each consumes 31 percent less energy and performs 4,7 percent better on SPECint and consumes 36 percent less energy and performs 7 percent better for SPECfp.

Cache memories are commonly implemented through multiple memory banks to improve bandwidth and latency. The early knowledge of the data cache bank that an instruction will access can help to improve the performance in several ways. One scenario that is likely to become increasingly important is clustered microprocessors with a distributed cache. This work presents a study of different cache bank predictors. We show that effective bank predictors can be implemented with relatively low cost. For instance, a predictor of approximately 4 Kbytes is shown to achieve an average hit rate of 78% for SPECint2000 when used to predict accesses to an 8-bank cache memory in a contemporary superscalar processor. We also show how a predictor can be used to reduce the communication latency caused by memory accesses in a clustered microarchitecture with a distributed cache design.