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남범석

Nam, Beomseok
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dc.citation.endPage 2271 -
dc.citation.number 8 -
dc.citation.startPage 2258 -
dc.citation.title IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS -
dc.citation.volume 26 -
dc.contributor.author Nam, Beomseok -
dc.contributor.author Kim, Jinwoong -
dc.contributor.author Jeong, Won-Ki -
dc.date.accessioned 2023-12-22T01:06:49Z -
dc.date.available 2023-12-22T01:06:49Z -
dc.date.created 2014-12-19 -
dc.date.issued 2015-08 -
dc.description.abstract Inherently multi-dimensional n-ary indexing structures such as R-trees are not well suited for the GPU because of their irregular memory access patterns and recursive back-tracking function calls. It has been known that traversing hierarchical tree structures in an irregular manner makes it difficult to exploit parallelism and to maximize the utilization of GPU processing units. Moreover, the recursive tree search algorithms often fail with large indexes because of the GPU's tiny runtime stack size. In this paper, we propose a novel parallel tree traversal algorithm-massively parallel restart scanning (MPRS) for multi-dimensional range queries that avoids recursion and irregular memory access. The proposed MPRS algorithm traverses hierarchical tree structures with mostly contiguous memory access patterns without recursion, which offers more chances to optimize the parallel SIMD algorithm. We implemented the proposed MPRS range query processing algorithm on n-ary bounding volume hierarchies including R-trees and evaluated its performance using real scientific datasets on an NVIDIA Tesla M2090 GPU. Our experiments show braided parallel SIMD friendly MPRS range query algorithm achieves at least 80 percent warp execution efficiency while task parallel tree traversal algorithm shows only 9-15 percent efficiency. Moreover, braided parallel MPRS algorithm accesses 7-20 times less amount of global memory than task parallel parent link algorithm by virtue of minimal warp divergence. -
dc.identifier.bibliographicCitation IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, v.26, no.8, pp.2258 - 2271 -
dc.identifier.doi 10.1109/TPDS.2014.2347041 -
dc.identifier.issn 1045-9219 -
dc.identifier.scopusid 2-s2.0-84937396536 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/9530 -
dc.identifier.url http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6876171 -
dc.identifier.wosid 000358226400016 -
dc.language 영어 -
dc.publisher IEEE COMPUTER SOC -
dc.title Exploiting Massive Parallelism for Indexing Multi-dimensional Datasets on the GPU -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Computer Science, Theory & Methods; Engineering, Electrical & Electronic -
dc.relation.journalResearchArea Computer Science; Engineering -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Parallel multi-dimensional indexing -
dc.subject.keywordAuthor multi-dimensional range query -
dc.subject.keywordAuthor GPGPU -
dc.subject.keywordPlus R-TREES -

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