  Current auto-tuning frameworks struggle with tuning computer systems
configurations due to their large parameter space, complex interdependencies,
and high evaluation cost. Utilizing probabilistic models, Structured Bayesian
Optimization (SBO) has recently overcome these difficulties. SBO decomposes the
parameter space by utilizing contextual information provided by system experts
leading to fast convergence. However, the complexity of building probabilistic
models has hindered its wider adoption. We propose BoAnon, a SBO framework that
learns the system structure from its logs. BoAnon provides an API enabling
experts to encode knowledge of the system as performance models or components
dependency. BoAnon takes in the learned structure and transforms it into a
probabilistic graph model. Then it applies the expert-provided knowledge to the
graph to further contextualize the system behavior. BoAnon probabilistic graph
allows the optimizer to find efficient configurations faster than other
methods. We evaluate BoAnon via a hardware architecture search problem,
achieving an improvement in energy-latency objectives ranging from $5-7$
x-factors improvement over the default architecture. With its novel contextual
structure learning pipeline, BoAnon makes using SBO accessible for a wide range
of other computer systems such as databases and stream processors.
