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Efficient out-of-core sorting algorithms for the Parallel Disks Model
Authors:Vamsi Kundeti  Sanguthevar Rajasekaran[Author vitae]
Affiliation:aDepartment of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
Abstract:In this paper,1 we present efficient algorithms for sorting on the Parallel Disks Model (PDM). Numerous asymptotically optimal algorithms have been proposed in the literature. However, many of these merge based algorithms have large underlying constants in the time bounds, because they suffer from the lack of read parallelism on the PDM. The irregular consumption of the runs during the merge affects the read parallelism and contributes to the increased sorting time. In this paper, we first introduce a novel idea called the dirty sequence accumulation that improves the read parallelism. Next, we show analytically that this idea can reduce the number of parallel I/O’s required to sort the input close to the lower bound of View the MathML source. We verify experimentally our dirty sequence idea with the standard R-Way merge and show that our idea can reduce the number of parallel I/Os to sort on the PDM significantly.
Keywords:Algorithms  External memory  Sorting  Massive data sets  Parallel disk model  Disk sorting  Randomized algorithms
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