Vantage: Scalable and Efficient Fine-Grain Cache Partitioning

Cache partitioning has a wide range of uses in CMPs, from guaran-teeing quality of service and controlled sharing to security-related techniques. However, existing cache partitioning schemes (such as way-partitioning) are limited to coarse-grain allocations, can only support few partitions, and reduce cache associativity, hurting per-formance. Hence, these techniques can only be applied to CMPs with 2-4 cores, but fail to scale to tens of cores. We present Vantage, a novel cache partitioning technique that overcomes the limitations of existing schemes: caches can have tens of partitions with sizes specified at cache line granularity, while maintaining high associativity and strong isolation among parti-tions. Vantage leverages cache arrays with good hashing and asso-ciativity, which enable soft-pinning a large portion of cache lines. It enforces capacity allocations by controlling the replacement pro-cess. Unlike prior schemes, Vantage provides strict isolation guar-antees by partitioning most (e.g. 90%) of the cache instead of all of it. Vantage is derived from analytical models, which allow us to provide strong guarantees and bounds on associativity and siz-ing independent of the number of partitions and their behaviors. It is simple to implement, requiring around 1.5 % state overhead and simple changes to the cache controller. We evaluate Vantage using extensive simulations. On a 32-core system, using 350 multiprogrammed workloads and one partition per core, partitioning the last-level cache with conventional tech-niques degrades throughput for 71 % of the workloads versus an unpartitioned cache (by 7 % average, 25 % maximum degradation), even when using 64-way caches. In contrast, Vantage improves throughput for 98 % of the workloads, by 8 % on average (up to 20%), using a 4-way cache