Model-Based Algorithm Selection Techniques
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Date
2019-09-18
Authors
Srinivasan, Sudharshan
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
With significant research going into the development of scientific software
over the years, there exist a plethora of toolkits using different algorithms to solve
the same problem. But the performance of these toolkits are very much problem
specific and depend on multiple factors, including experimental setup and hardware
configurations. This makes it very difficult to choose a suitable software beforehand
without testing them for specific problems while the wrong choice of software
contributes to severe performance downgrade. In this thesis, we address this
selection challenge by proposing a faster and reliable model-based approach instead
of empirically running time-consuming experiments to select suitable software
toolkits. In specific, we would be looking at selection for two classes of algorithms
that solve parallel graph processing applications and systems of linear equations.
Appropriate metrics have also been introduced to evaluate the quality of selection
techniques.
Description
Keywords
Iterative solvers, Learning to rank, Parallel graph processing