Storing a collection of polygons using quadtrees
ACM Transactions on Graphics (TOG)
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Volcano— An Extensible and Parallel Query Evaluation System
IEEE Transactions on Knowledge and Data Engineering
Super-Scalar RAM-CPU Cache Compression
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
GPUQP: query co-processing using graphics processors
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Breaking the memory wall in MonetDB
Communications of the ACM - Surviving the data deluge
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Using graphics processors for high performance IR query processing
Proceedings of the 18th international conference on World wide web
Relational query coprocessing on graphics processors
ACM Transactions on Database Systems (TODS)
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Database compression on graphics processors
Proceedings of the VLDB Endowment
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
Efficient co-processor utilization in database query processing
Information Systems
Why it is time for a HyPE: a hybrid query processing engine for efficient GPU coprocessing in DBMS
Proceedings of the VLDB Endowment
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The diversity of hardware components within a single system calls for strategies for efficient cross-device data processing. For example, existing approaches to CPU/GPU co-processing distribute individual relational operators to the "most appropriate" device. While pleasantly simple, this strategy has a number of problems: it may leave the "inappropriate" devices idle while overloading the "appropriate" device and putting a high pressure on the PCI bus. To address these issues we distribute data among the devices by partially decomposing relations at the granularity of individual bits. Each of the resulting bit-partitions is stored and processed on one of the available devices. Using this strategy, we implemented a processor for spatial range queries that makes efficient use of all available devices. The performance gains achieved indicate that bitwise distribution makes a good cross-device processing strategy.