The paper presents a new data partitioning algorithm for parallel computing on heterogeneous processors. Like traditional functional partitioning algorithms, the algorithm assumes ...
— Massive data analysis on large clusters presents new opportunities and challenges for query optimization. Data partitioning is crucial to performance in this environment. Howev...
The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining ta...
Ying Chen, Frank K. H. A. Dehne, Todd Eavis, Andre...
This paper presents a general methodology for the efficient parallelization of existing data cube construction algorithms. We describe two different partitioning strategies, one f...
Frank K. H. A. Dehne, Todd Eavis, Susanne E. Hambr...
GPU-based heterogeneous clusters continue to draw attention from vendors and HPC users due to their high energy efficiency and much improved single-node computational performance...