  Interactive applications incorporating high-data rate sensing and computer
vision are becoming possible due to novel runtime systems and the use of
parallel computation resources. To allow interactive use, such applications
require careful tuning of multiple application parameters to meet required
fidelity and latency bounds. This is a nontrivial task, often requiring expert
knowledge, which becomes intractable as resources and application load
characteristics change. This paper describes a method for automatic performance
tuning that learns application characteristics and effects of tunable
parameters online, and constructs models that are used to maximize fidelity for
a given latency constraint. The paper shows that accurate latency models can be
learned online, knowledge of application structure can be used to reduce the
complexity of the learning task, and operating points can be found that achieve
90% of the optimal fidelity by exploring the parameter space only 3% of the
time.
