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Program power profiling based on phase behaviors
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DOI：10.1016/j.suscom.2018.05.001.png)
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Power profiling tools based on fast and accurate workload analysis can be useful for job scheduling and resource allocation aiming to optimize the power consumption of large-scale, high-performance computer systems. In this article, we propose a novel method for predicting the power consumption of a complete workload or application by extrapolating the power consumption of only a few code segments of the same application obtained from measurements. As such, it provides a fast and yet effective way for predicting the power consumption of the execution of both single and mull-threaded programs on arbitrary architectures without having to profile the entire program's execution. The latter would be costly to obtain, especially if it is a long-running program. Our method employs a set of code analysis tools to capture the program's phase behavior and then uses a multi-variable linear regression method to estimate the power consumption of the entire program. For validation, we select the SPEC 2006 benchmark suite and the NAS parallel benchmarks to evaluate the accuracy and effectiveness of our method. Experimental results on three generations of multicore processors show that our power profiling method achieves good accuracy in predicting program's energy use with relatively small errors.
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