- Title Information
- Title
- Multilevel Techniques for Compression and Reduction of Scientific Data
- Name:
Personal
- Name Part
- Whitney, Ben Ensrud
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Ainsworth, Mark
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Darbon, Jérôme
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Klasky, Scott
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Department of Applied Mathematics
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2018
- Physical Description
- Extent
- xii, 107 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2018
- Genre (aat)
- theses
- Abstract
- Computational simulation pervades science and engineering, enabling new insights about complex physical phenomena. The advent of exascale computer systems, which will enable simulation at an even greater scale, poses new challenges, motivating methods to cope with vastly increased amounts of simulation data. Enter data compression and reduction. Of particular interest are multilevel and hierarchical reduction methods, which split input data into a sequence of components tailored to the heterogeneous storage media of modern machines. Such methods are especially appropriate when the applications for which the data are to be used require varying levels of resolution. For instance, one user might require that the reduced dataset meet a prescribed error tolerance for the calculation of some derived quantity, while another might need it to fit in the available RAM for a responsive visualization.
The first topic of this dissertation is the analysis of a simple lossless compression method based on decimation. Bounds on the expected compression ratio are derived and numerical illustrations of the performance of the technique are presented. The remainder of the dissertation is devoted to a suite of multilevel adaptive techniques inspired by the orthogonal and hierarchical decompositions. First, univariate algorithms for the two use cases of constrained loss and constrained storage are designed. Next, the technique is extended to the multivariate setting, and a loss estimator permitting the control of pointwise errors is introduced. Finally, a related loss estimator for Sobolev norms is used to perform reduction while limiting distortion in quantities of interest.
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01041273")
- Topic
- Numerical analysis
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00887919")
- Topic
- Data compression (Computer science)
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20180618
- Identifier:
DOI
- 10.26300/ya1v-hn97
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations