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Understanding on-node application power and performance characteristics is critical to the push toward exascale computing. In this paper, we present an analysis of factors that impact both performance and energy usage of OpenMP applications. Using hardware performance counters in the Intel Sandy bridge X86-64 architecture, we measure energy usage and power draw for a variety of OpenMP programs: simple micro-benchmarks, a task parallel benchmark suite, and a hydrodynamics mini-app of a few thousand lines. The evaluation reveals substantial variations in energy usage depending on the algorithm, the compiler, the optimization level, the number of threads, and even the temperature of the chip. Variations of 20% were common and in the extreme were over 2X. In most cases, performance increases and energy usage decreases as more threads are used. However, for programs with sub-linear speedup, minimal energy usage often occurs at a lower thread count than peak performance. Our findings informed the design and implementation of an adaptive run time system that automatically throttles concurrency using data measured on-line from hardware performance counters. Without source code changes or user intervention, the thread scheduler accurately decides when energy can be conserved by limiting the number of active threads. For the target programs, dynamic runtime throttling consistently reduces power and overall energy usage by up to 3%.
Porterfield et al. (Wed,) studied this question.