English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Current auto-tuning frameworks struggle with tuning computer systemsconfigurations due to their large parameter space, complex interdependencies,and high evaluation cost. Utilizing probabilistic models, Structured BayesianOptimization (SBO) has recently overcome these difficulties. SBO decomposes theparameter space by utilizing contextual information provided by system expertsleading to fast convergence. However, the complexity of building probabilisticmodels has hindered its wider adoption. We propose BoAnon, a SBO framework thatlearns the system structure from its logs. BoAnon provides an API enablingexperts to encode knowledge of the system as performance models or componentsdependency. BoAnon takes in the learned structure and transforms it into aprobabilistic graph model. Then it applies the expert-provided knowledge to thegraph to further contextualize the system behavior. BoAnon probabilistic graphallows the optimizer to find efficient configurations faster than othermethods. 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 contextualstructure learning pipeline, BoAnon makes using SBO accessible for a wide rangeof other computer systems such as databases and stream processors.

BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters