Show simple item record

dc.contributor.authorHe, Jianen_US
dc.date.accessioned2014-03-14T20:19:20Z
dc.date.available2014-03-14T20:19:20Z
dc.date.issued2007-11-15en_US
dc.identifier.otheretd-11292007-160738en_US
dc.identifier.urihttp://hdl.handle.net/10919/29786
dc.description.abstractThe present work aims at an efficient, portable, and robust design of a data-distributed massively parallel DIRECT, the deterministic global optimization algorithm widely used in multidisciplinary engineering design, biological science, and physical science applications. The original algorithm is modified to adapt to different problem scales and optimization (exploration vs.\ exploitation) goals. Enhanced with a memory reduction technique, dynamic data structures are used to organize local data, handle unpredictable memory requirements, reduce the memory usage, and share the data across multiple processors. The parallel scheme employs a multilevel functional and data parallelism to boost concurrency and mitigate the data dependency, thus improving the load balancing and scalability. In addition, checkpointing features are integrated to provide fault tolerance and hot restarts. Important algorithm modifications and design considerations are discussed regarding data structures, parallel schemes, error handling, and portability. Using several benchmark functions and real-world applications, the present work is evaluated in terms of optimization effectiveness, data structure efficiency, memory usage, parallel performance, and checkpointing overhead. Modeling and analysis techniques are used to investigate the design effectiveness and performance sensitivity under various problem structures, parallel schemes, and system settings. Theoretical and experimental results are compared for two parallel clusters with different system scale and network connectivity. An analytical bounding model is constructed to measure the load balancing performance under different schemes. Additionally, linear regression models are used to characterize two major overhead sources---interprocessor communication and processor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of high-dimensional problems and large scale systems, the data-distributed massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the generalized design considerations and analysis techniques are beneficial for transforming many global search algorithms to become effective large scale parallel optimization tools.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartthesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectthe DIRECT algorithmen_US
dc.subjectparallel schemesen_US
dc.subjectdynamic data structuresen_US
dc.subjectglobal optimizationen_US
dc.titleDesign and Evaluation of a Data-distributed Massively Parallel Implementation of a Global Optimization Algorithm---DIRECTen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairWatson, Layne T.en_US
dc.contributor.committeememberSandu, Adrianen_US
dc.contributor.committeememberShaffer, Clifford A.en_US
dc.contributor.committeememberRibbens, Calvin J.en_US
dc.contributor.committeememberNachlas, Joel A.en_US
dc.contributor.committeememberBeattie, Christopher A.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-11292007-160738/en_US
dc.date.sdate2007-11-29en_US
dc.date.rdate2008-01-12
dc.date.adate2008-01-12en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record