  The scale of scientific High Performance Computing (HPC) and High Throughput
Computing (HTC) has increased significantly in recent years, and is becoming
sensitive to total energy use and cost. Energy-efficiency has thus become an
important concern in scientific fields such as High Energy Physics (HEP). There
has been a growing interest in utilizing alternate architectures, such as low
power ARM processors, to replace traditional Intel x86 architectures.
Nevertheless, even though such solutions have been successfully used in mobile
applications with low I/O and memory demands, it is unclear if they are
suitable and more energy-efficient in the scientific computing environment.
Furthermore, there is a lack of tools and experience to derive and compare
power consumption between the architectures for various workloads, and
eventually to support software optimizations for energy efficiency. To that
end, we have performed several physical and software-based measurements of
workloads from HEP applications running on ARM and Intel architectures, and
compare their power consumption and performance. We leverage several profiling
tools (both in hardware and software) to extract different characteristics of
the power use. We report the results of these measurements and the experience
gained in developing a set of measurement techniques and profiling tools to
accurately assess the power consumption for scientific workloads.
