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Author

Nam, Beomseok
Data Intensive Computing Lab
Research Interests
  • Distributed and parallel computing, high performance computing, database systems, OS and storage systems

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Parallel multi-dimensional range query processing with R-trees on GPU

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Title
Parallel multi-dimensional range query processing with R-trees on GPU
Author
Kim, JinwoongKim, Sul-GiNam, Beomseok
Keywords
CUDA; GPGPU; GPU; Parallel indexing; Parallel multi-dimensional indexing; Parallel R-tree
Issue Date
201308
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Citation
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, v.73, no.8, pp.1195 - 1207
Abstract
The general purpose computing on graphics processing unit (GP-GPU) has emerged as a new cost effective parallel computing paradigm in high performance computing research that enables large amount of data to be processed in parallel. Large scale scientific data intensive applications have been playing an important role in modern high performance computing research. A common access pattern into such scientific data analysis applications is multi-dimensional range query, but not much research has been conducted on multi-dimensional range query on the GPU. Inherently multi-dimensional indexing trees such as R-Trees are not well suited for GPU environment because of its irregular tree traversal. Traversing irregular tree search path makes it hard to maximize the utilization of massively parallel architectures. In this paper, we propose a novel MPTS (Massively Parallel Three-phase Scanning) R-tree traversal algorithm for multi-dimensional range query, that converts recursive access to tree nodes into sequential access. Our extensive experimental study shows that MPTS R-tree traversal algorithm on NVIDIA Tesla M2090 GPU consistently outperforms traditional recursive R-trees search algorithm on Intel Xeon E5506 processors.
URI
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DOI
http://dx.doi.org/10.1016/j.jpdc.2013.03.015
ISSN
0743-7315
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