Characterizing the energy consumption of data transfers and arithmetic operations on x86−64 processors

The energy efficiency of computer systems is influenced by many interdependent aspects. To asses the efficiency, typical benchmarks characterized the total power consumption of a computer system under certain domain specific workloads. For example, in case of the SPECPower benchmark the workload is a typical web server specific Java application. The contribution of individual components is usually not considered in this class of benchmarks. The CPU makes the most significant contribution due to both its high peak power consumption and the high variability depending on the workload. Correlations of workload and energy consumption of parts of the processors are usually done with simulations rather than actual measurements. This is mainly a consequence of the limited time resolution of power meters that is usually orders of magnitude too low to observe variations in the time scale of microarchitectural events. Furthermore, it is usually not possible to solely measure power consumption of processors as they are supplied by multiple power lines that are not easily accessible and are often shared with other components. In this paper we present benchmarks and a measurement methodology that compensate for the time resolution of our power meter by applying a constant and well-defined workload to the system. Using this experimental setup we analyze x86−64 microarchitectures from AMD and Intel. We furthermore characterize the contribution of individual operations and data transfers to the total power consumption of the Intel system.