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Computational Geometry in C
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
QPipe: a simultaneously pipelined relational query engine
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Introduction to Data Mining, (First Edition)
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GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A view of the parallel computing landscape
Communications of the ACM - A View of Parallel Computing
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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HPCA '11 Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture
BWS: balanced work stealing for time-sharing multicores
Proceedings of the 7th ACM european conference on Computer Systems
Towards building a high performance spatial query system for large scale medical imaging data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Robust and efficient polygon overlay on parallel stream processors
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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As an important application of spatial databases in pathology imaging analysis, cross-comparing the spatial boundaries of a huge amount of segmented micro-anatomic objects demands extremely data- and compute-intensive operations, requiring high throughput at an affordable cost. However, the performance of spatial database systems has not been satisfactory since their implementations of spatial operations cannot fully utilize the power of modern parallel hardware. In this paper, we provide a customized software solution that exploits GPUs and multi-core CPUs to accelerate spatial cross-comparison in a cost-effective way. Our solution consists of an efficient GPU algorithm and a pipelined system framework with task migration support. Extensive experiments with real-world data sets demonstrate the effectiveness of our solution, which improves the performance of spatial cross-comparison by over 18 times compared with a parallelized spatial database approach.