Partitioned Parallel Radix Sort

Lee, Shin-Jae; Jeon, Minsoo; Kim, Dongseung; Sohn, Andrew

doi:10.1006/jpdc.2001.1808

Cited by 34 publications

(13 citation statements)

References 10 publications

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“…In the implementation of sample sort, three different sample counts of 2P(P-1), logN, and N of the total N data are employed to represent light [16], mid-range [9], and optimal rates [12], respectively. Work load is best balanced with N sampling rate among the three.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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High-speed parallel external sorting of data with arbitrary distribution

Jeon¹,

Kim²

2004

IJHPCN

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Abstract. Many parallel sorting algorithms of (external) disk data have been reported such as NOWsort, SPsort, and hill sort, etc. They all reduce the execution time compared to some known sequential sort; however, they differ in terms of the speed, throughput, and cost-effectiveness. Mostly they deal with data that are uniformly distributed in their value range. If we divide and redistribute data to processors using the fixed and equal division of the key range, all processors will have about equal number of keys to sort and store. But if irregularly distributed data are given, the performance will suffer severely since the partitioning would no longer produce balanced load among processors. Few research results have been yet reported for parallel external sort of data with arbitrary distribution. In this paper, we develop two distribution-insensitive scalable parallel external sorting algorithms that use sampling technique and histogram counts to achieve even distribution of data, which eventually contribute to achieve superb performance. Experimental results on a cluster of 16 Linux workstations show up to three-fold enhancement of the performance compared to previous NOW-sort for sorting 16GB integer keys.

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Section: Experiments and Discussionmentioning

confidence: 99%

“…It differs from the sample sort in the way to find the splitters: it employs histogram instead of sampling [6,10]. The algorithm executes in two phases too: histogram computation and bucket sort phase (phase 1), and data distribution and post-processing phase (phase 2).…”

Section: Histogram-based Parallel External Sortmentioning

confidence: 99%

High-speed parallel external sorting of data with arbitrary distribution

Jeon¹,

Kim²

2004

IJHPCN

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“…In general, the implementation cannot assume that a full, three-dimensional field will fit in local memory on a single node, so the sorting must be undertaken in parallel. An appropriate algorithm for this sorting is the partitioned parallel radix sort [Lee et al, 2002]. This sorting algorithm effectives creates an approximate histogram of the data (in this case the density field), assigns buckets on that histogram to individual processors in order to ensure rough load balancing, and then uses a serial sorting algorithm on each processor to fully sort the density field.…”

Section: Available Potential Energymentioning

confidence: 99%

Simulation of the Navier–Stokes equations in three dimensions with a spectral collocation method

Subich

Lamb

Stastna

2013

Numerical Methods in Fluids

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SUMMARYThis paper describes a nonlinear, three‐dimensional spectral collocation method for the simulation of the incompressible Navier–Stokes equations under the Boussinesq approximation, motivated by geophysical and environmental flows. These flows are driven by the interaction of stratified fluid with topography, which this model accurately accounts for by using a mapped coordinate system. The spectral collocation method is implemented with both a Fourier trigonometric expansion and the Chebyshev polynomials, as appropriate for the domain boundary conditions. The coordinate mapping prohibits the use of existing, fast solution methods that rely on the separation of variables, so a preconditioner based on the approximate solution of a corresponding finite‐difference problem with geometric multigrid is used. The model is parallelized with the Message Passing Interface library, and it runs effectively on shared and distributed‐memory systems. Copyright © 2013 John Wiley & Sons, Ltd.

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“…To speedup the sorting multiprocessors are employed for parallel sorting. Several parallel sorting algorithms such as bitonic sort [1,6], sample sort [5], column sort [3] and parallel radix sort [7,8] have been devised. Parallel sorts usually need a fixed number of data exchange and merging operations.…”

Section: Introductionmentioning

confidence: 99%

Parallelizing Merge Sort onto Distributed Memory Parallel Computers

Jeon

Kim

2002

Lecture Notes in Computer Science

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Abstract. Merge sort is useful in sorting a great number of data progressively, especially when they can be partitioned and easily collected to a few processors. Merge sort can be parallelized, however, conventional algorithms using distributed memory computers have poor performance due to the successive reduction of the number of participating processors by a half, up to one in the last merging stage. This paper presents load-balanced parallel merge sort where all processors do the merging throughout the computation. Data are evenly distributed to all processors, and every processor is forced to work in all merging phases. An analysis shows the upper bound of the speedup of the merge time as (P − 1)/ log P where P is the number of processors. We have reached a speedup of 8.2 (upper bound is 10.5) on 32-processor Cray T3E in sorting of 4M 32-bit integers.

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Partitioned Parallel Radix Sort

Cited by 34 publications

References 10 publications

High-speed parallel external sorting of data with arbitrary distribution

High-speed parallel external sorting of data with arbitrary distribution

Simulation of the Navier–Stokes equations in three dimensions with a spectral collocation method

Parallelizing Merge Sort onto Distributed Memory Parallel Computers

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