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Modern computer systems are highly configurable, with the total variabilityspace sometimes larger than the number of atoms in the universe. Understandingand reasoning about the performance behavior of highly configurable systems,over a vast and variable space, is challenging. State-of-the-art methods forperformance modeling and analyses rely on predictive machine learning models,therefore, they become (i) unreliable in unseen environments (e.g., differenthardware, workloads), and (ii) may produce incorrect explanations. To tacklethis, we propose a new method, called Unicorn, which (i) captures intricateinteractions between configuration options across the software-hardware stackand (ii) describes how such interactions can impact performance variations viacausal inference. We evaluated Unicorn on six highly configurable systems,including three on-device machine learning systems, a video encoder, a databasemanagement system, and a data analytics pipeline. The experimental resultsindicate that Unicorn outperforms state-of-the-art performance debugging andoptimization methods in finding effective repairs for performance faults andfinding configurations with near-optimal performance. Further, unlike theexisting methods, the learned causal performance models reliably predictperformance for new environments.

Unicorn: Reasoning about Configurable System Performance through the lens of Causality