  Energy efficiency is an essential requirement for all contemporary computing
systems. We thus need tools to measure the energy consumption of computing
systems and to understand how workloads affect it. Significant recent research
effort has targeted direct power measurements on production computing systems
using on-board sensors or external instruments. These direct methods have in
turn guided studies of software techniques to reduce energy consumption via
workload allocation and scaling. Unfortunately, direct energy measurements are
hampered by the low power sampling frequency of power sensors. The coarse
granularity of power sensing limits our understanding of how power is allocated
in systems and our ability to optimize energy efficiency via workload
allocation.
  We present ALEA, a tool to measure power and energy consumption at the
granularity of basic blocks, using a probabilistic approach. ALEA provides
fine-grained energy profiling via statistical sampling, which overcomes the
limitations of power sensing instruments. Compared to state-of-the-art energy
measurement tools, ALEA provides finer granularity without sacrificing
accuracy. ALEA achieves low overhead energy measurements with mean error rates
between 1.4% and 3.5% in 14 sequential and parallel benchmarks tested on both
Intel and ARM platforms. The sampling method caps execution time overhead at
approximately 1%. ALEA is thus suitable for online energy monitoring and
optimization. Finally, ALEA is a user-space tool with a portable,
machine-independent sampling method. We demonstrate two use cases of ALEA,
where we reduce the energy consumption of a k-means computational kernel by 37%
and an ocean modelling code by 33%, compared to high-performance execution
baselines, by varying the power optimization strategy between basic blocks.

Performance