A hypergraph-partitioning based remapping model for image-space parallel volume rendering

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buir.advisorAykanat, Cevdet
dc.contributor.authorCambazoğlu, Berkant Barla
dc.date.accessioned2016-01-08T20:17:06Z
dc.date.available2016-01-08T20:17:06Z
dc.date.copyright2000
dc.date.issued2000
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2000.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2000.en_US
dc.descriptionIncludes bibliographical references (leaves 72-76).en_US
dc.descriptionCataloged from PDF version of article.
dc.description.abstractRay-casting is a popular direct volume rendering technique, used to explore the content of 3D data. Although this technique is capable of producing high quality visualizations, its slowness prevents the interactive use. The major method to overcome this speed limitation is parallelization. In this work, we investigate the image-space parallelization of ray-casting for distributed memory architectures. The most important issues in image-space parallelization are load balancing and minimization of the data redistribution overhead introduced at successive visualization instances. Load balancing in volume rendering requires the estimation of screen work load correctly. For this purpose, we tested three different load assignment schemes. Since the data used in this work is made up of unstructured tetrahedral grids, clusters of data were used instead of cells, for efficiency purposes. Two different cluster-processor distribution schemes are employed to see the effects of initial data distribution. The major contribution of the thesis comes at the hypergraph partitioning model proposed as a solution to the remapping problem. For this purpose, existing hypergraph partitioning tool PaToH is modified and used as a one-phase remapping tool. The model is tested on a Parsytec CC system and satisfactory results are obtained. Compared to the two-phase jagged partitioning model, our work incurs less preprocessing overhead. At comparable load imbalance values, our hypergraph partitioning model requires 25% less total volume of communication than jagged partitioning on the average.
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Berkant Barla Cambazoğluen_US
dc.format.extentxiv, 78 leaves : illustrations, charts ; 30 cm.en_US
dc.identifier.itemidBILKUTUPB051146
dc.identifier.urihttp://hdl.handle.net/11693/18191
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectImage-space parallelization
dc.subjectRay-casting
dc.subjectUnstructured grids
dc.subjectWork load assignment
dc.subjectHypergraph partitioning
dc.subjectLoad balancing
dc.subjectRemapping
dc.titleA hypergraph-partitioning based remapping model for image-space parallel volume renderingen_US
dc.title.alternativeGörüntü-uzayı paralel hacim görüntüleme için hiperçizge bölümlemeye dayalı yeniden eşleme modeli
dc.typeThesisen_US

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