2007 · 62 citations · 7 references
ComputingComputer ScienceHigh-performance ComputingComputer GraphicsDatabasesParallel ComputingAccelerator ArchitectureData ParallelismGraphics Processing Unit ArchitectureGraphics Processing Unit ComputingGraphics Processing UnitsQuery ProcessingData Parallel ExecutionGpu AcceleratorsGpu ArchitectureGpu Programming ModelsIndexesParallel Query ProcessingQuery Performance OptimizationRelational Query Processing
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.
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Naga K. Govindaraju, Jim Gray, Ritesh Kumar et al. · 2006 · 422 citations
Relational joins on graphics processors
Bingsheng He, Ke Yang, Rui Fang et al. · 2008 · 352 citations
Fast computation of database operations using graphics processors
Naga K. Govindaraju, Brandon Lloyd, Wei Wang et al. · 2004 · 276 citations
Cache Conscious Indexing for Decision-Support in Main Memory
Jun Rao, Kenneth A. Ross · 1998 · 249 citations · Full text