Multi-Core Program Optimization: Parallel Sorting Algorithms in Intel Cilk Plus

Saleem, Sabahat; Lali, M. IkramUllah; Nawaz, M. Saqib; Nauman, Abou Bakar

doi:10.14257/ijhit.2014.7.2.15

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Speedup of their parallel Introspective quicksort is 1.473. Saleem et al [12] estimated speedup of both quick and merge sort algorithms using Intel Cilk Plus. Ranokpanuwat and Kittitornkun [13] developed Parallel Partition and Merge Quick sort (PPMQSort).…”

Section: Related Workmentioning

confidence: 99%

Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

Ketchaya

Rattanatranurak

2022

ACI

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PurposeSorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them.Design/methodology/approachIn this paper, the algorithm named Dual Parallel Partition Sorting (DPPSort) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition (DPPartition). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff.FindingsIn the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively.Originality/valueThe authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.

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Section: Related Workmentioning

confidence: 99%

Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

Ketchaya

Rattanatranurak

2022

ACI

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“…In Ref. [28], Saleem et al parallelized Quicksort using Cilk Plus, which includes a similar pivoting operation with our CT construction. However, they did not realize very good speedups because they did not parallelize the pivoting operation.…”

Section: Related Workmentioning

confidence: 99%

Parallelization of Matrix Partitioning in Construction of Hierarchical Matrices using Task Parallel Languages

Bai

Hiraishi

Nakashima

et al. 2019

Journal of Information Processing

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A hierarchical matrix (H-matrix) is an approximated form that represents N × N correlations of N objects. H-matrix construction is achieved by partitioning a matrix into submatrices, followed by calculating the element values of these submatrices. This paper proposes implementations of matrix partitioning using task parallel languages, Cilk Plus and Tascell. Matrix partitioning is divided into two steps: cluster tree construction by dividing objects into clusters hierarchically, and block cluster tree construction by observing all cluster pairs at the same level of the cluster tree that satisfy an admissibility condition. As the two types of trees constructed and traversed in these steps are unpredictably unbalanced, it is expected that we can efficiently parallelize both these steps using task parallel languages. To obtain sufficient parallelism in the cluster tree construction, we not only execute recursive calls in parallel but also parallelize the inside of each recursive step. For the block cluster tree construction, we assigned each worker its own space so that the workers can store the cluster pairs without using locks. As a result, compared to a sequential implementation in C, we achieved up to an 11.5-fold speedup using Cilk Plus and a 12.6-fold speedup by Tascell for the cluster tree construction. For the block cluster tree construction, up to a 37.7-fold speedup by Cilk Plus and a 38.8-fold speedup using Tascell are achieved. In regard to the entire process of matrix partitioning, we achieved up to a 12.2-fold speedup by Cilk Plus and a 14.5-fold speedup using Tascell. All experiments were executed on two 18-core Xeon processors with real datasets to generate coefficient matrices used in the surface charge method.

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“…The partitions are sorted in parallel up to 8 threads. Saleem et al [12] used Intel Cilk Plus and estimated Speedup both quick sort and merge sort algorithm. Ranokpanuwat and Kittitornkun [13] proposed Parallel Partition and Merge Quick sort (PPMQSort) that executes on Shared memory/multicore system with OpenMp 3.0.…”

Section: Related Workmentioning

confidence: 99%

Dual Parallel Partition Sorting Algorithm

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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Sorting is the important algorithm which is widely used and implemented in many applications. This paper presents an efficient parallel sorting algorithm called Dual Parallel Partition sorting (DPPSort). The DPPSort partitions the data recursively and then sorts in parallel. The partitioning phase divides the data into two parts in parallel. In sorting phase, the Standard Template Library Sorting function (STLSort) and GNU sorting function (qsort) are integrated in this work. This work is developed in C/C++ language and linked with OpenMP library. In our experiments, the 4-core Intel i7-3770 with Ubuntu Linux systems is implemented. Our work is faster than qsort and STLSort function up to 5.95× and 4.70×, respectively.

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Multi-Core Program Optimization: Parallel Sorting Algorithms in Intel Cilk Plus

Abstract: Abstract

Cited by 8 publications

References 20 publications

Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

Parallelization of Matrix Partitioning in Construction of Hierarchical Matrices using Task Parallel Languages

Dual Parallel Partition Sorting Algorithm

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