Modern computer systems are highly configurable, with the variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the performance behavior of highly configurable systems, due to a vast variability space, is challenging. State-of-the-art methods for performance modeling and analyses rely on predictive machine learning models, therefore, they become (i) unreliable in unseen environments (e.g., different hardware, workloads), and (ii) produce incorrect explanations. To this end, we propose a new method, called Unicorn, which (a) captures intricate interactions between configuration options across the software-hardware stack and (b) describes how such interactions impact performance variations via causal inference. We evaluated Unicorn on six highly configurable systems, including three on-device machine learning systems, a video encoder, a database management system, and a data analytics pipeline. The experimental results indicate that Unicorn outperforms state-of-the-art performance optimization and debugging methods. Furthermore, unlike the existing methods, the learned causal performance models reliably predict performance for new environments.

本文提出了一种名为Unicorn的新方法，它可以捕捉软件-硬件堆栈中配置选项之间的复杂交互，并描述了这些交互如何通过因果推断影响性能变化，可以胜任高度可配置系统的性能问题发现和优化，而且相比现有方法，学到的因果性能模型可以可靠地为新环境预测性能。

独角兽：通过因果关系来推理可配置系统的性能