Architecture slack exploitation for phase classification and performance estimation in server-class processors

Abstract

In this paper, we present a highly accurate performance estimation methodology that accounts for architecture slack in workloads. Our work leverages the advanced instrumentation available in POWER8 processor that monitors core pipeline activity in relation to off-core memory accesses to build metrics for architecture slack characterization for workloads. Using these metrics, we construct a workload classifier that classifies workloads as core-bound and memory-bound and propose a performance prediction model for change in processor frequency for each class of workload – cPerf and mPerf, respectively. We evaluated these models with SPECCPU and PARSEC benchmark suites on a POWER8 based OpenPOWER system. We observed that the predicted performance with our models has high accuracy (97%) for both CPU and memory intensive benchmarks. We validated that the classifier is suitable to accurately classify phase of workloads during execution intervals. We developed an algorithm that uses classifier for phase classification and prediction models for performance estimation at runtime. We leveraged this algorithm and evaluated the execution time impacts of CPU and memory classified benchmarks.