Iceberg-cube computation with PC clusters
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 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
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
MOLAP cube based on parallel scan algorithm
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Real-time computation of advanced rules in OLAP databases
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Task scheduling for GPU accelerated OLAP systems
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
High-performance online spatial and temporal aggregations on multi-core CPUs and many-core GPUs
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
Why it is time for a HyPE: a hybrid query processing engine for efficient GPU coprocessing in DBMS
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
GPGPU (General Purpose Graphical Processing Unit) programming is receiving more attention recently because of enormous computations speed up offered by this technology. GPGPU is applied in many branches of science and industry not excluding databases, even if this is not the primary field of expected benefits. In this paper a typical time consuming database algorithm, i.e. OLAP cube creation, implemented on GPU is compared to its CPU counterpart by analysis of performance, scalability, programming and optimisation ease. Results are discussed formulating roadmap for future GPGPU applications in databases.