JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

Handle URI:
http://hdl.handle.net/10754/575241
Title:
JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure
Authors:
Labschutz, Matthias; Bruckner, Stefan; Groller, M. Eduard; Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Rautek, Peter
Abstract:
Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure 2015:1 IEEE Transactions on Visualization and Computer Graphics
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Visualization and Computer Graphics
Issue Date:
12-Aug-2015
DOI:
10.1109/TVCG.2015.2467331
Type:
Article
ISSN:
1077-2626
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7192686
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLabschutz, Matthiasen
dc.contributor.authorBruckner, Stefanen
dc.contributor.authorGroller, M. Eduarden
dc.contributor.authorHadwiger, Markusen
dc.contributor.authorRautek, Peteren
dc.date.accessioned2015-08-19T12:27:02Zen
dc.date.available2015-08-19T12:27:02Zen
dc.date.issued2015-08-12en
dc.identifier.citationJiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure 2015:1 IEEE Transactions on Visualization and Computer Graphicsen
dc.identifier.issn1077-2626en
dc.identifier.doi10.1109/TVCG.2015.2467331en
dc.identifier.urihttp://hdl.handle.net/10754/575241en
dc.description.abstractSparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7192686en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleJiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structureen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphicsen
dc.eprint.versionPost-printen
dc.contributor.institutionUniversity of Bergenen
dc.contributor.institutionTU Wien and VrVis Research Centeren
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorLabschutz, Matthiasen
kaust.authorHadwiger, Markusen
kaust.authorRautek, Peteren
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