ScaleOPT: A Scalable Optimal Page Replacement Policy Simulator

This paper proposes a scalable optimal page replacement policy (OPT) simulator called ScaleOPT to scale the OPT simulation by leveraging multi-core parallelism. Specifically, we first propose AccessMap which collects the future references of pages in parallel before the OPT simulation. It enables calculating the next reference time of the accessed page in a constant time. Second, we introduce Pipelined-Tree consisting of multiple trees which organize caches based on min-max reference times. It enables traverse and update operations of each cache to be performed in a partially parallel manner (i.e., pipelined), thereby scaling out the OPT simulation on multi-cores. Finally, we implement ScaleOPT with two techniques and evaluate it on a 72-core machine. The experimental results demonstrate that ScaleOPT improves the simulation time by up to 6.3×, 7.7×, 20.5×, and 13.9× compared with the long-established standard algorithm-based simulator along with our AccessMap, a variable-size cache scheme, and two widely-used cache simulators (webcachesim and libCacheSim), respectively.