LAUSR

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 are concurrency and orthogonality. 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.