Unicorn: Reasoning about Configurable System Performance through the lens of Causality. (arXiv:2201.08413v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2201.08413


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