Probabilistic Replacement: Enabling Flexible Use of Shared Caches for CMPs

Abstract

CMPs allow threads to share portions of the on-chip cache. Critical to successful sharing are the policies for allocating the shared cache to threads. Researchers have observed the best allocation policy often depends on the degree of cache interference. Typically, workloads with high cache interference require explicit working set isolation via cache partitioning, while workloads with low cache interference perform well without explicit allocation-i.e., using LRU. While cache interference impacts cache allocation policies, relatively little is known about its root causes. This paper investigates how different sharing patterns in multiprogrammed workloads give rise to varying degrees of cache interference. We show cache interference is tied to the granularity of interleaving amongst inter-thread memory references: ne-grain interleaving yields high cache interference while coarse-grain interleaving yields low cache differences in mapping and timing of per-thread references in the cache. For example, coarse-grain interleaving occurs anytime per-thread references map to disjoint cache sets, or are performed during distinct time intervals. We quantify such spatial and temporal isolation of per-thread memory references, and correlate its degree to LRU and partitioning performance. This paper also proposes probabilistic replacement (PR), a new cache allocation policy motivated by our reference interleaving insights. PR controls the rate at which inter-thread replacements transfer cache resources between threads, and hence, the per-thread cache allocation boundary. Because PR operates on inter-thread replacements, it adapts to cache interference. When interleaving is coarse-grained (low interference), inter-thread replacements are rare, so PR reverts to LRU. As interleaving becomes more ne-grained (higher interference), inter-thread replacements increase, and PR in turn creates more resistance to impede cache allocation. Our results show PR outperforms LRU, UCP, and an ideal cache partitioning technique by 4.86%, 3.15%, and 1.09%, respectively.