Partitioned Parallel Radix Sort

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

doi:10.1007/3-540-39999-2_14

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…After the initialization or the shuffling evolution loop, the initial or evolved complexes should be sorted in increasing objective function values and rearranged according to their corresponding objective function values. Because the sorting and rearranging processes are inherently parallel, we should parallelize these processes by using the radix parallel sorting method [20,21] and the parallel rearranging method. The parallel sorting and rearranging are also implemented on the multi-core CPU and the many-core GPU.…”

Section: Parallel Sorting and Rearranging Of Complexesmentioning

confidence: 99%

Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU

Kan

Liang

et al. 2016

Advances in Meteorology

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The famous global optimization SCE-UA method, which has been widely used in the field of environmental model parameter calibration, is an effective and robust method. However, the SCE-UA method has a high computational load which prohibits the application of SCE-UA to high dimensional and complex problems. In recent years, the hardware of computer, such as multi-core CPUs and many-core GPUs, improves significantly. These much more powerful new hardware and their software ecosystems provide an opportunity to accelerate the SCE-UA method. In this paper, we proposed two parallel SCE-UA methods and implemented them on Intel multi-core CPU and NVIDIA many-core GPU by OpenMP and CUDA Fortran, respectively. The Griewank benchmark function was adopted in this paper to test and compare the performances of the serial and parallel SCE-UA methods. According to the results of the comparison, some useful advises were given to direct how to properly use the parallel SCE-UA methods.

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Section: Parallel Sorting and Rearranging Of Complexesmentioning

confidence: 99%

Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU

Kan

Liang

et al. 2016

Advances in Meteorology

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“…The results show that for small data sets compared to the number of processors, the MPI implementation is the faster while for large data sets, the shared memory implementation is faster. Shin-Jae Lee [3] solved the load imbalance problem present in parallel radix sort. Redistributing the keys in each round of radix, each processor has exactly the same number of keys, thereby reducing the overall sorting time.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Modified Pure Radix Sort for Large Heterogeneous Data Set

Shukla¹,

Anil²,

Saxena³

2012

IOSRJCE

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We have proposed a Modified Pure Radix Sort for Large Heterogeneous Data Set. In this research paper we discuss the problems of radix sort, brief study of previous works of radix sort & present new modified pure radix sort algorithm for large heterogeneous data set. We try to optimize all related problems of radix sort through this algorithm. This algorithm works on the Technology of Distributed Computing which is implemented on the principal of divide & conquer method.

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“…span [7,38,31], and there are a variety of practical parallel implementations that have been developed [7,38,36,29,25,37,31,18,21,27,30,10,19,35,34]. However, 0 0 2 0 3 2 1 1 1 3 1…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Theoretically-Efficient and Practical Parallel In-Place Radix Sorting

Obeya

Kahssay

Fan

et al. 2019

The 31st ACM Symposium on Parallelism in Algorithms and Architectures

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Radix sort stands out for having a better worse case theoretical bound than any comparison-based sort, for fixed length integers. Despite the fact that radix sort can be implemented either in-place or in parallel, there exists no parallel in-place implementation for radix sort that guarantees a sub-linear worst case span. The challenge arises due to read-write races when reading from and writing to the same array. In this thesis, I introduce Regions sort and use it to implement a parallel work-efficient in-place radix sorting algorithm. To sort integers from a range , and a parameter , the algorithm requires only (log log) auxiliary memory, (log) work and ((/ + log) log) span. Regions sort uses a divide-and-conquer paradigm. It first divides the array into sub-arrays, each of which is sorted independently in parallel. This decreases the irregularity in the input and reorders it into a set of regions, each of which has similar properties. Next, it builds a data structure, called the regions graph to represent the needed data movements to completely sort the array. Finally, Regions sort iteratively plans swaps that satisfies the required data movements. The algorithm then has to recurse on records with so-far equal keys to break ties. I compare two variants of Regions sort with the state-of-the-art sorting integer sort and comparison sort algorithms. Experiments show that the best variant of Regions sort usually outperforms other sorting algorithms. In addition, I perform different experiments showing that Regions sort maintains its superiority regardless input distribution and range. More importantly, the single-threaded implementation of Regions sort outperforms the highly optimized Ska Sort implementation for serial in-place radix sort.

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Partitioned Parallel Radix Sort

Cited by 10 publications

References 7 publications

Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU

Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU

Modified Pure Radix Sort for Large Heterogeneous Data Set

Theoretically-Efficient and Practical Parallel In-Place Radix Sorting

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