An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Techniques for mapping tasks to machines in heterogeneous computing systems
Journal of Systems Architecture: the EUROMICRO Journal - Heterogeneous distributed and parallel architectures: hardware, software and design tools
Efficient string matching: an aid to bibliographic search
Communications of the ACM
Efficient Organization of Large Multidimensional Arrays
Proceedings of the Tenth International Conference on Data Engineering
Process Partitioning for Distributed Embedded Systems
CODES '96 Proceedings of the 4th International Workshop on Hardware/Software Co-Design
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Computing Multidimensional Aggregates in Parallel
ICPADS '98 Proceedings of the 1998 International Conference on Parallel and Distributed Systems
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Cache-oblivious string dictionaries
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Why you should run TPC-DS: a workload analysis
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Exploring graphics processing units as parallel coprocessors for online aggregation
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
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
Task scheduling for GPU accelerated OLAP systems
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
Optimizing index deployment order for evolving OLAP
Proceedings of the 15th International Conference on Extending Database Technology
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
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OLAP (On-Line Analytical Processing) is an approach to efficiently evaluate multidimensional data for business intelligence applications. OLAP contributes to business decision-making by identifying, extracting, and analyzing multidimensional data. The fundamental structure of OLAP is a data cube that enables users to interactively explore the distinct data dimensions. Processing depends on the complexity of queries, dimensionality, and growing size of the data cube. As data volumes keep on increasing and the demands by business users also increase, higher processing speed than ever is needed, as faster processing means faster decisions and more profit to industry.In this paper, we are proposing an Adaptive Hybrid OLAP Architecture that takes advantage of heterogeneous systems with GPUs and CPUs and leverages their different memory subsystems characteristics to minimize response time. Thus, our approach (a)聽exploits both types of hardware rather than using the CPU only as a frontend for GPU; (b)聽uses two different data formats (multidimensional cube and relational cube) to match the GPU and CPU memory access patterns and diverts queries adaptively to the best resource for solving the problem at hand; (c)聽exploits data locality of multidimensional OLAP on NUMA multicore systems through intelligent thread placement; and (d)聽guides its adaptation and choices by an architectural model that captures the memory access patterns and the underlying data characteristics.Results show an increase in performance by roughly four folds over the best known related approach. There is also the important economical factor. The proposed hybrid system costs only 10聽% more than same system without GPU. With this small extra cost, the added GPU increases query processing by almost 2聽times.