There is a performance-at-all-costs mentality at most of the nation's
supercomputing centers that has resulted in significant and growing
energy use.  This unchecked consumption costs the government a
considerable amount of money and wastes natural resources.  Moreover,
energy consumption and the resultant heat dissipation are becoming
important performance-limiting factors that we believe will eventually
come to bear on high-performance computing users.  The goal of our
research is to consume less energy (generating less heat) with no more
than a modest performance penalty and to do so without burdening
computational scientists.

This talk investigates the energy consumption and execution time of
applications from a standard benchmark suite (NAS) on a power-scalable
cluster.  Our results show that many standard scientific applications
executed on a such cluster can save energy by scaling the processor
down to lower energy levels, without a significant increase in time.
Additionally, this talk presents several runtime techniques for
controlling power consumption and increasing the energy efficiency of
application without undue performance penalties.  Furthermore, this
talk shows how to both consume less energy and increase performance in
a cluster by increasing the parallelism (more nodes) while
simultaneously decreasing the individual node performance.