Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
GPU-based computation of distance functions on road networks with applications
Proceedings of the 2009 ACM symposium on Applied Computing
Efficient approximate entity extraction with edit distance constraints
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Data Parallel Bin-Based Indexing for Answering Queries on Multi-core Architectures
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Relational query coprocessing on graphics processors
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Generalizing prefix filtering to improve set similarity joins
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Multidimensional data structures for spatial applications
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Database compression on graphics processors
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Efficient similarity joins for near-duplicate detection
ACM Transactions on Database Systems (TODS)
Speeding up large-scale geospatial polygon rasterization on GPGPUs
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Combining CPU and GPU architectures for fast similarity search
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GPU-Based influence regions optimization
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Speeding up large-scale point-in-polygon test based spatial join on GPUs
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Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation
MICRO-45 Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture
Indexing methods for moving object databases: games and other applications
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
The HELLS-join: a heterogeneous stream join for extremely large windows
Proceedings of the Ninth International Workshop on Data Management on New Hardware
Super-EGO: fast multi-dimensional similarity join
The VLDB Journal — The International Journal on Very Large Data Bases
The Yin and Yang of processing data warehousing queries on GPU devices
Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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ACM Transactions on Embedded Computing Systems (TECS)
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Information Sciences: an International Journal
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A similarity join operation A BOWTIEepsiv B takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.