Bootstrapping Parameter Space Exploration for Fast Tuning
Published: Jun 12, 2018
Abstract
The task of tuning parameters for optimizing performance or other metrics of interest such as energy, variability, etc. can be resource and time consuming. Presence of a large parameter space makes a comprehensive exploration infeasible. In this paper, we propose a novel bootstrap scheme, called GEIST, for parameter space exploration to find performance-optimizing configurations quickly. Our scheme represents the parameter space as a graph whose...
Paper Details
Title
Bootstrapping Parameter Space Exploration for Fast Tuning
Published Date
Jun 12, 2018
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