A Study of Power-Performance Modeling Using a Domain-Specific Language

Abstract

Energy use is now a first-class design constraint in high-performance systems and applications. Improving our understanding of application energy consumption in diverse, heterogeneous systems will be essential to efficient operation. For example, power limits in large scale parallel and distributed systems will require optimizing performance under energy constraints. However, with increased levels of parallelism, complex memory hierarchies, hardware heterogeneity, and diverse programming models and interfaces, improving performance and energy efficiency simultaneously is exceedingly difficult. Our thesis is that estimating energy use, either a priori or as soon as possible at runtime, will be essential to future systems. Such estimates must adapt with changes in applications across hardware configurations. Existing approaches offer insight and detail, but typically are too cumbersome to enable adaptation at runtime or lack portability or accuracy. To overcome these limitations, we propose two energy estimation techniques which use the Aspen domain specific language for performance modeling: ACEE (Algorithmic and Categorical Energy Estimation), a combination of analytical and empirical modeling techniques embedded in a runtime framework that leverages Aspen, and AEEM (Aspen's Embedded Energy Modeling), a system level coarse-grained energy estimation technique that uses performance modeling from Aspen to generate energy estimations at runtime. This paper presents methodology of the models and examines their accuracy as well as their advantages and challenges in several use cases.