Towards Online Application Cache Behaviors Identification in CMPs

On chip multiprocessors (CMPs) platforms, multiple co-scheduled applications can severely degrade performance and quality of service (QoS) when they contend for last-level cache (LLC) resources. Whether an application will impose destructive interference on co-scheduled applications is largely dependent on its own inherent cache access behavior characteristics. In this work, we first present case studies that show how inter-application interferences result in undesirable performance in both shared and private cache based LLC designs. We then propose a new online approach for application cache behavior identification on the basis of detailed simulation and analysis with SPEC CPU2006 benchmarks. We demonstrate that our approach can more concisely identify application cache behaviors. Moreover, the proposed approach can be implemented directly in hardware to dynamically identify the application cache behaviors at runtime. Finally, we show with two case studies that how the proposed approach can be adopted by both shared and private based cache sharing mechanisms, i.e. cache partitioning algorithms (CPAs) and cache spilling techniques, for more concise cache resource management.