Efficient out-of-core sorting algorithms for the Parallel Disks Model
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 Omega (log(M/B) (N/M)). 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. (C) 2011 Elsevier Inc. All rights reserved.
sorting algorithms parallel disk model external memory massive data sets disk sorting randomized algorithms