Fast Parallel Sorting Algorithms on GPUs

Jan, Bilal; Montrucchio, Bartolomeo; Ragusa, Carlo Stefano; Khan, Fiaz Gul; Khan, Omar

doi:10.5121/ijdps.2012.3609

Cited by 16 publications

(6 citation statements)

References 18 publications

(17 reference statements)

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“…At the moment, the world is increasingly investigating a variety of robotics problems [9][10][11][12]. One of the most interesting is to determine the current position of the lidar.…”

Section: Analysis Of Literary Sourcesmentioning

confidence: 99%

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

Mochurad

Kryvinska

2021

Symmetry

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The problem of determining the position of the lidar with optimal accuracy is relevant in various fields of application. This is an important task of robotics that is widely used as a model when planning the route of vehicles, flight control systems, navigation systems, machine learning, and managing economic efficiency, a study of land degradation processes, planning and control of agricultural production stages, land inventory to evaluations of the consequences of various environmental impacts. The paper provides a detailed analysis of the proposed parallelization algorithm for solving the problem of determining the current position of the lidar. To optimize the computing process in order to accelerate and have the possibility of obtaining a real-time result, the OpenMP parallel computing technology is used. It is also possible to significantly reduce the computational complexity of the successive variant. A number of numerical experiments on the multi-core architecture of modern computers have been carried out. As a result, it was possible to accelerate the computing process about eight times and achieve an efficiency of 0.97. It is shown that a special difference in time of execution of a sequential and parallel algorithm manages to increase the number of measurements of lidar and iterations, which is relevant in simulating various problems of robotics. The obtained results can be substantially improved by selecting a computing system where the number of cores is more than eight. The main areas of application of the developed method are described, its shortcomings and prospects for further research are provided.

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“…At the moment, the world is increasingly investigating a variety of robotics problems [9][10][11][12]. One of the most interesting is to determine the current position of the lidar.…”

Section: Analysis Of Literary Sourcesmentioning

confidence: 99%

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

Mochurad

Kryvinska

2021

Symmetry

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“…Jan et al [19] presented a new parallel algorithm named the min-max butterfly network, for searching a minimum and maximum in an important number of elements collections. They presented a comparative analysis of the new parallel algorithm and three parallel sorting algorithms (odd even sort, bitonic sort and rank sort) in terms of sorting rate, sorting time and speed running on the CPU and GPU platforms.…”

Section: Background and Related Workmentioning

confidence: 99%

A Comparative Study of Sorting Algorithms with FPGA Acceleration by High Level Synthesis

Jmaa¹,

Atitallah²,

Duvivier³

et al. 2019

CyS

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Nowadays, sorting is an important operation for several real-time embedded applications. It is one of the most commonly studied problems in computer science. It can be considered as an advantage for some applications such as avionic systems and decision support systems because these applications need a sorting algorithm for their implementation. However, sorting a big number of elements and/or real-time decision making need high processing speed. Therefore, accelerating sorting algorithms using FPGA can be an attractive solution. In this paper, we propose an efficient hardware implementation for different sorting algorithms (BubbleSort, InsertionSort, SelectionSort, QuickSort, HeapSort, ShellSort, MergeSort and TimSort) from high-level descriptions in the zynq-7000 platform. In addition, we compare the performance of different algorithms in terms of execution time, standard deviation and resource utilization. From the experimental results, we show that the SelectionSort is 1.01-1.23 times faster than other algorithms when N < 64; Otherwise, TimSort is the best algorithm.

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“…Here for finding best solution between two candidate solutions, the solution with minimum constraint violation is preferred than the solution with more constraint violation and if the number of constraints violated by both solutions is same or zero the solution with minimum objective function value is preferred over another in case of minimization function and the solution with maximum problem value is preferred than the other solution in case of maximization problem. For finding this minimum Odd-Even sort [18] is used as atomic minimum CUDA functions are available only for GPGPU with compute capability above 3.5. Fig.…”

Section: Proposed Gpgpu Based Multi-hive Abc Algorithmmentioning

confidence: 99%

GPGPU based Multi-hive ABC Algorithm for Constrained Global Optimization Problems

Mane¹,

Adamuthe²,

Pawar³

2018

EAI Endorsed Transactions on Energy Web

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INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

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Fast Parallel Sorting Algorithms on GPUs

Abstract: ABSTRACT

Cited by 16 publications

References 18 publications

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

A Comparative Study of Sorting Algorithms with FPGA Acceleration by High Level Synthesis

GPGPU based Multi-hive ABC Algorithm for Constrained Global Optimization Problems

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