Accurate and Reliable Energy Measurement and Modelling of Data Transfer Between CPU and GPU in Parallel Applications on Heterogeneous Hybrid Platforms
Hafiz Adnan Niaz, Ravi Reddy Manumachu, Alexey Lastovetsky · IEEE Transactions on Computers · 2024
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.