Fast, Accurate Processor Evaluation Through Heterogeneous, Sample-Based Benchmarking

Abstract

Performance evaluation is a key task in computing and communication systems. Benchmarking is one of the most common techniques for evaluation purposes, where the performance of a set of representative applications is used to infer system responsiveness in a general usage scenario. Unfortunately, most benchmarking suites are limited to a reduced number of applications, and in some cases, rigid execution configurations. This makes it hard to extrapolate performance metrics for a general-purpose architecture, supposed to have a multi-year lifecycle, running dissimilar applications concurrently. The main culprit of this situation is that current benchmark-derived metrics lack generality, statistical soundness and fail to represent general-purpose environments. Previous attempts to overcome these limitations through random app mixes significantly increase computational cost (workload population shoots up), making the evaluation process barely affordable. To circumvent this problem, in this article we present a more elaborate performance evaluation methodology named BenchCast. Our proposal provides more representative performance metrics, but with a drastic reduction of computational cost, limiting app execution to a small and representative fraction marked through code annotation. Thanks to this labeling and making use of synchronization techniques, we generate heterogeneous workloads where every app runs simultaneously inside its Region Of Interest, making a few execution seconds highly representative of full application execution.