  Scale-out workloads like media streaming or Web search serve millions of
users and operate on a massive amount of data, and hence, require enormous
computational power. As the number of users is increasing and the size of data
is expanding, even more computational power is necessary for powering up such
workloads. Data centers with thousands of servers are providing the
computational power necessary for executing scale-out workloads. As operating
data centers requires enormous capital outlay, it is important to optimize them
to execute scale-out workloads efficiently. Server processors contribute
significantly to the data center capital outlay, and hence, are a prime
candidate for optimizations. While data centers are constrained with power, and
power consumption is one of the major components contributing to the total cost
of ownership (TCO), a recently-introduced scale-out design methodology
optimizes server processors for data centers using performance per unit area.
In this work, we use a more relevant performance-per-power metric as the
optimization criterion for optimizing server processors and reevaluate the
scale-out design methodology. Interestingly, we show that a scale-out processor
that delivers the maximum performance per unit area, also delivers the highest
performance per unit power.
