  Energy efficiency and energy conservation are one of the most crucial
constraints for meeting the 20MW power envelope desired for exascale systems.
Towards this, most of the research in this area has been focused on the
utilization of user-controllable hardware switches such as per-core dynamic
voltage frequency scaling (DVFS) and software controlled clock modulation at
the application level. In this paper, we present a tuning plugin for the
Periscope Tuning Framework which integrates fine-grained autotuning at the
region level with DVFS and uncore frequency scaling (UFS). The tuning is based
on a feed-forward neural network which is formulated using Performance
Monitoring Counters (PMC) supported by x86 systems and trained using
standardized benchmarks. Experiments on five standardized hybrid benchmarks
show an energy improvement of 16.1% on average when the applications are tuned
according to our methodology as compared to 7.8% for static tuning.
