Deciphering predictive schedulers for heterogeneous-ISA multicore architectures

Abstract

Heterogeneous architectures have become increasingly common. From co-packaging small and large cores, to GPUs alongside CPUs, to general-purpose heterogeneous-ISA architectures with cores implementing different ISAs. As diversity of execution cores grows, predictive models become of paramount importance for scheduling and resource allocation. In this paper, we investigate the capabilities of performance predictors in a heterogeneous-ISA setting, as well as the predictors' effects on scheduler quality. We follow an unbiased feature selection methodology to identify the optimal set of features for this task, instead of pre-selecting features before training. We propose metrics that bridge the gap between traditional prediction accuracy metrics and a scheduler's performance. We further present our evaluation methodology, which was meticulously designed with this study in mind, and finally, we incorporate our findings in ML-based schedulers and evaluate their sensitivity to the underlying system's level of heterogeneity.