Data warehousing and OLAP for decision support
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics
GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
The cgmCUBE project: Optimizing parallel data cube generation for ROLAP
Distributed and Parallel Databases
Designing efficient sorting algorithms for manycore GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
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
Exploring graphics processing units as parallel coprocessors for online aggregation
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
GPU-WAH: applying GPUs to compressing bitmap indexes with word aligned hybrid
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Comparing GPU and CPU in OLAP cubes creation
SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
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In Online Analytical Processing (OLAP) users view data through a multidimensional model known as the data cube, allowing the aggregation of information along different attributes and operations such as slicing and dicing. In-memory OLAP systems keep all relevant data in main memory and also support efficient updates of cube data, enabling interactive planning, forecasting, and what-if analysis. Since usually only the base data is stored and all aggregations and other calculations are computed on the fly, complex computations may seriously downgrade performance. We present an approach that uses graphics processing units (GPUs) as parallel coprocessors for high performance in-memory OLAP operations. In particular, our method accelerates the calculation of compute-intensive rules, which represent business dependencies that are more complex than mere aggregates. In addition to the data structures and algorithms, we describe how to extend the approach to multi-GPU systems in order to scale it to larger data sets.