Energy predictive models employing performance events have emerged as a promising alternative to other mainstream methods for developing software power meters used in runtime energy profiling of applications. These models are cost-effective and provide a highly accurate means of measuring the energy consumption of applications during execution. Recently, software power meters have been proposed to profile the dynamic energy consumption of data transfers between CPU and GPU in heterogeneous hybrid platforms, thereby effectively addressing the gap between software power meters that measure computations and those that measure data transfers. However, the state-of-the-art software power meters lack fundamental properties essential for achieving accurate runtime energy profiling of parallel hybrid programs on heterogeneous hybrid servers. Two critical properties areconcurrencyandorthogonality. In this work, we define these essential properties and propose a methodology for developing concurrent and orthogonal platform-level software power meters capable of accurate runtime energy profiling of parallel hybrid programs on heterogeneous hybrid servers. We apply this methodology to develop software power meters for three heterogeneous hybrid servers that consist of Intel multicore CPUs and Nvidia GPUs from different generations. Furthermore, we demonstrate the accuracy and efficiency of the proposed software power meters by using them to estimate the dynamic energy consumption of computation and communication activities in three parallel hybrid programs. Our results show that the average prediction error for dynamic energy consumption by these software power meters is just 2.5% across our servers.

Developing energy-efficient software that leverages application-level energy optimization techniques is essential to tackle the pressing technological challenge of energy efficiency on modern heterogeneous computing platforms. While energy modelling and optimization of computations have received considerable attention in energy research, there remains a significant gap in the energy modelling of data transfer between computing devices on heterogeneous hybrid platforms. Our study aims to fill this crucial gap. In this work, we comprehensively study the energy consumption of data transfer between a host CPU and a GPU accelerator on heterogeneous hybrid platforms using the three mainstream energy measurement methods: (a) System-level physical measurements based on external power meters (ground-truth), (b) Measurements using on-chip power sensors, and (c) Energy predictive models. The ground-truth method is accurate but prohibitively time-consuming. While the on-chip sensors in Intel multicore CPU processors are inaccurate, the Nvidia GPU sensors do not capture data transfer activity. Therefore, we focus on the third approach and propose a novel methodology to select a small subset of performance events that effectively capture all the energy consumption activities during a data transfer and develop accurate linear energy predictive models employing the shortlisted performance events. Finally, we develop independent and accurate runtime pluggable software energy sensors based on our proposed energy predictive models that employ disjoint sets of performance events to estimate the dynamic energy of computations and data transfers. We employ the sensors to predict the energy consumption of computations and data transfer between a host CPU and two A40 Nvidia GPUs in three parallel scientific applications, and the high accuracy (average prediction error of 5%) of our sensors’ predictions further underscores their practical relevance.