The continuing miniaturization and parallelization of computer hardware has facilitated the development of mobile and
field-deployable systems that can accommodate terascale processing within once prohibitively small size and weight
constraints. General-purpose Graphics Processing Units (GPUs) are prominent examples of such terascale devices.
Unfortunately, the added computational capability of these devices often comes at the cost of larger demands on power,
an already strained resource in these systems. This study explores power versus performance issues for a workload that
can take advantage of GPU capability and is targeted to run in field-deployable environments, i.e., Synthetic Aperture
Radar (SAR). Specifically, we focus on the Image Formation (IF) computational phase of SAR, often the most compute
intensive, and evaluate two different state-of-the-art GPU implementations of this IF method. Using real and simulated
data sets, we evaluate performance tradeoffs for single- and double-precision versions of these implementations in terms
of time-to-solution, image output quality, and total energy consumption. We employ fine-grain direct-measurement
techniques to capture isolated power utilization and energy consumption of the GPU device, and use general and radarspecific
metrics to evaluate image output quality. We show that double-precision IF can provide slight image
improvement to low-reflective areas of SAR images, but note that the added quality may not be worth the higher power
and energy costs associated with higher precision operations.