Abstract
In recent years, computer hardware technology has greatly developed especially large memory and multi-core, but algorithm efficiency is not beneficial from the development of hardware. The fundamental reason is that there is insufficient utilizing CPU cache, as well as the limitations of single-thread programming. In the field of data warehousing and OLAP, data cube computing is an important and time-consuming operation, how to improve efficiency of data cube calculation is continuing to pursue goals. Based on the characteristics of modern CPU, we have proposed two parallel algorithms TASK_PMW and DATA_SSMW, TASK_PMW is task-based division of the parallel algorithm, each CPU core is responsible for one Cuboid; DATA_SSMW is data partition, and scanned sharing raw data, ensure load balancing, has good scalability and high efficient. Through experiments on dual-core CPU, TASK_PMW improve 1/3, DATA_SSMW 2/3 than the original algorithm.
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Zhou, G., Zhang, H. (2011). Multi-core CPU Based Parallel Cube Algorithms. In: Shen, G., Huang, X. (eds) Advanced Research on Computer Science and Information Engineering. CSIE 2011. Communications in Computer and Information Science, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21411-0_8
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DOI: https://doi.org/10.1007/978-3-642-21411-0_8
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