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Auto-tuning on the macro scale : high level algorithmic auto-tuning for scientific applications

Author(s)
Chan, Cy P
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Alternative title
High level algorithmic auto-tuning for scientific applications
Other Contributors
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Alan Edelman.
Terms of use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
In this thesis, we describe a new classification of auto-tuning methodologies spanning from low-level optimizations to high-level algorithmic tuning. This classification spectrum of auto-tuning methods encompasses the space of tuning parameters from low-level optimizations (such as block sizes, iteration ordering, vectorization, etc.) to high-level algorithmic choices (such as whether to use an iterative solver or a direct solver). We present and analyze four novel auto-tuning systems that incorporate several techniques that fall along a spectrum from the low-level to the high-level: i) a multiplatform, auto-tuning parallel code generation framework for generalized stencil loops, ii) an auto-tunable algorithm for solving dense triangular systems, iii) an auto-tunable multigrid solver for sparse linear systems, and iv) tuned statistical regression techniques for fine-tuning wind forecasts and resource estimations to assist in the integration of wind resources into the electrical grid. We also include a project assessment report for a wind turbine installation for the City of Cambridge to highlight an area of application (wind prediction and resource assessment) where these computational auto-tuning techniques could prove useful in the future.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (p. 102-107).
 
Date issued
2012
URI
http://hdl.handle.net/1721.1/74980
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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