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The scale of scientific High Performance Computing (HPC) and High ThroughputComputing (HTC) has increased significantly in recent years, and is becomingsensitive to total energy use and cost. Energy-efficiency has thus become animportant concern in scientific fields such as High Energy Physics (HEP). Therehas been a growing interest in utilizing alternate architectures, such as lowpower ARM processors, to replace traditional Intel x86 architectures.Nevertheless, even though such solutions have been successfully used in mobileapplications with low I/O and memory demands, it is unclear if they aresuitable and more energy-efficient in the scientific computing environment.Furthermore, there is a lack of tools and experience to derive and comparepower consumption between the architectures for various workloads, andeventually to support software optimizations for energy efficiency. To thatend, we have performed several physical and software-based measurements ofworkloads from HEP applications running on ARM and Intel architectures, andcompare their power consumption and performance. We leverage several profilingtools (both in hardware and software) to extract different characteristics ofthe power use. We report the results of these measurements and the experiencegained in developing a set of measurement techniques and profiling tools toaccurately assess the power consumption for scientific workloads.

Techniques and tools for measuring energy efficiency of scientific software applications