Performance Modeling and Prediction for Dense Linear Algebra

Published: Jun 1, 2017
Abstract
This dissertation introduces measurement-based performance modeling and prediction techniques for dense linear algebra algorithms. As a core principle, these techniques avoid executions of such algorithms entirely, and instead predict their performance through runtime estimates for the underlying compute kernels. For a variety of operations, these predictions allow to quickly select the fastest algorithm configurations from available...
Paper Details
Title
Performance Modeling and Prediction for Dense Linear Algebra
Published Date
Jun 1, 2017
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