Reisert, Patrick ; Calotoiu, Alexandru ; Shudler, Sergei ; Wolf, Felix (2017)
Following the Blind Seer - Creating Better Performance Models Using Less Information.
23rd International European Conference on Parallel and Distributed Computing (Euro-Par 2017). Santiago de Compostela, Spanien (28. 08.-01.09.2017)
doi: 10.1007/978-3-319-64203-1_8
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Kurzbeschreibung (Abstract)

Offering insights into the behavior of applications at higher scale, performance models are useful for finding performance bugs and tuning the system. Extra-P, a tool for automated performance modeling, uses statistical methods to automatically generate, from a small number of performance measurements, models that can be used to predict performance where no measurements are available. However, the current version requires the manual pre-configuration of a search space, which might turn out to be unsuitable for the problem at hand. Furthermore, noise in the data often leads to models that indicate a worse behavior than there actually is. In this paper, we propose a new model-generation algorithm that solves both of the above problems: The search space is built and automatically refined on demand, and a scale-independent error metric tells both when to stop the refinement process and whether a model reflects faithfully enough the behavior the data exhibits. This makes Extra-P easier to use, while also allowing it to produce more accurate results. Using data from previous case studies, we show that the mean relative prediction error decreases from 46% to 13%.

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