Cache Conscious Indexing for Decision-Support in Main Memory
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Hardware acceleration for spatial selections and joins
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
Fast and approximate stream mining of quantiles and frequencies using graphics processors
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
CAM conscious integrated answering of frequent elements and top-k queries over data streams
Proceedings of the 4th international workshop on Data management on new hardware
Data Parallel Bin-Based Indexing for Answering Queries on Multi-core Architectures
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
GPU-accelerated predicate evaluation on column store
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Ameliorating memory contention of OLAP operators on GPU processors
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
X-device query processing by bitwise distribution
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
Parallel approaches to machine learning-A comprehensive survey
Journal of Parallel and Distributed Computing
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We present GPUQP, a relational query engine that employs both CPUs and GPUs (Graphics Processing Units) for in-memory query co-processing. GPUs are commodity processors traditionally designed for graphics applications. Recent research has shown that they can accelerate some database operations orders of magnitude over CPUs. So far, there has been little work on how GPUs can be programmed for heavy-duty database constructs, such as tree indexes and joins, and how well a full-fledged GPU query co-processor performs in comparison with their CPU counterparts. In this work, we explore the design decisions in using GPUs for query co-processing using both a graphics API and a general purpose programming model. We then demonstrate the processing flows as well as the performance results of our methods.