• Title/Summary/Keyword: parallel algorithms

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A Parallel Algorithm for Image Segmentation on Mesh-connected MIMD System

  • Jeon, Byeong-Moon;Jeong, Chang-Sung
    • Journal of Korea Society of Industrial Information Systems
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    • v.3 no.1
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    • pp.258-268
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    • 1998
  • This paper presents two sequential advanced split and merge algorithms and a parallel image segmentation algorithm based on them. First, the two advanced methods are obtained from the combination of edge detection and classic split and merge to solve the inherent problems of the classical method. Besides, the parallel image segmentation algorithm on mesh-connected MIMD system considers three types in the merge stage to reduce the communication overhead between processors, such as intraprocessor merge, interprocessor with boundary merge, and interprocessor without boundary merge. Finally , we prove that the proposed algorithms achieve the improved performance by implementing them.

Parallel Machines Scheduling with Rate-Modifying Activities to Minimize Makespan (Rate-Modifying 활동이 있는 병렬기계의 Makespan 최소화를 위한 일정 계획)

  • Cho, Hang-Min;Yim, Seung-Bin;Jeong, In-Jae
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.30 no.2
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    • pp.44-50
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    • 2007
  • This paper deals with the problem of scheduling jobs and rate-modifying activities on parallel machines. A rate-modifying activity is an activity that changes the production rate of equipment such as maintenance and readjustment. If a job is scheduled after the rate-modifying activity, then the processing time varies depending on the modifying rate of the activity. In this study, we extend the single machine problem to parallel machines problem and propose algorithms is to schedule the rate-modifying activities and jobs to minimize the makespan on parallel machines which is NP-hard. We propose a branch and bound algorithm with three lower bounds to solve medium size problems optimally. Also we develop three heuristics, Modified Longest Processing Time, Modified MULTIFIT and Modified COMBINE algorithms to solve large size problems. The test results show that branch and bound algorithm finds the optimal solution in a reasonable time for medium size problems (up to 15 jobs and 5 machines). For large size problem, Modified COMBINE and Modified MULTIFIT algorithms outperform Modified LPT algorithm in terms of solution quality.

Design of Parallel Algorithms for Conventional Matched-Field Processing over Array of DSP Processors (다중 DSP 프로세서 기반의 병렬 수중정합장처리 알고리즘 설계)

  • Kim, Keon-Wook
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.44 no.4 s.316
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    • pp.101-108
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    • 2007
  • Parallel processing algorithms, coupled with advanced networking and distributed computing architectures, improve the overall computational performance, dependability, and versatility of a digital signal processing system In this paper, novel parallel algorithms are introduced and investigated for advanced sonar algorithm, conventional matched-field processing (CMFP). Based on a specific domain, each parallel algorithm decomposes the sequential workload in order to obtain scalable parallel speedup. Depending on the processing requirement of the algorithm, the computational performance of the parallel algorithm reveals different characteristics. The high-complexity algorithm, CMFP shows scalable parallel performance on the array of DSP processors. The impact on parallel performance due to workload balancing, communication scheme, algorithm complexity, processor speed, network performance, and testbed configuration is explored.

The Implementation of Fast Object Recognition Using Parallel Processing on CPU and GPU (CPU와 GPU의 병렬 처리를 이용한 고속 물체 인식 알고리즘 구현)

  • Kim, Jun-Chul;Jung, Young-Han;Park, Eun-Soo;Cui, Xue-Nan;Kim, Hak-Il;Huh, Uk-Youl
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.5
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    • pp.488-495
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    • 2009
  • This paper presents a fast feature extraction method for autonomous mobile robots utilizing parallel processing and based on OpenMP, SSE (Streaming SIMD Extension) and CUDA programming. In the first step on CPU version, the algorithms and codes are optimized and then implemented by parallel processing. The parallel algorithms are debugged to maintain the same level of performance and the process for extracting key points and obtaining dominant orientation with respect to key points is parallelized. After extraction, a parallel descriptor via SSE instructions is constructed. And the GPU version also implemented by parallel processing using CUDA based on the SIFT. The GPU-Parallel descriptor achieves an acceleration up to five times compared with the CPU-Parallel descriptor, but it shows the lower performance than CPU version. CPU version also speed-up the four and half times compared with the original SIFT while maintaining robust performance.

Parallel Thinniing Algorithm using Weighted-Value (가중치를 이용한 병렬 세선화 알고리즘)

  • Han, Nak-Hee;Rhee, Phil-Kyu
    • Korean Journal of Cognitive Science
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    • v.7 no.1
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    • pp.5-35
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    • 1996
  • This paper addresses an one-pass parallel thinning algorithm which shows effectiveness in both accuracy and speed. The proposed method is based on parallel iterative boundary removal.Image connectivity are preseved and the algorithms performance is compared to other algorithms especially to parallel thinning algorithm which is the best parallel algorithm have been proposed.Evaluation result shows that the proposed algorithm compare favorably to others.The result shows exact thinning free from one pixel boundary noise and free from distortion of shape.

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RECENT ADVANCES IN DOMAIN DECOMPOSITION METHODS FOR TOTAL VARIATION MINIMIZATION

  • LEE, CHANG-OCK;PARK, JONGHO
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.24 no.2
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    • pp.161-197
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    • 2020
  • Total variation minimization is standard in mathematical imaging and there have been numerous researches over the last decades. In order to process large-scale images in real-time, it is essential to design parallel algorithms that utilize distributed memory computers efficiently. The aim of this paper is to illustrate recent advances of domain decomposition methods for total variation minimization as parallel algorithms. Domain decomposition methods are suitable for parallel computation since they solve a large-scale problem by dividing it into smaller problems and treating them in parallel, and they already have been widely used in structural mechanics. Differently from problems arising in structural mechanics, energy functionals of total variation minimization problems are in general nonlinear, nonsmooth, and nonseparable. Hence, designing efficient domain decomposition methods for total variation minimization is a quite challenging issue. We describe various existing approaches on domain decomposition methods for total variation minimization in a unified view. We address how the direction of research on the subject has changed over the past few years, and suggest several interesting topics for further research.

Optimization of Fuzzy Set Fuzzy Model by Means of Hierarchical Fair Competition-based Parallel Genetic Algorithms (계층적 경쟁기반 병렬 유전자 알고리즘을 이용한 퍼지집합 퍼지모델의 최적화)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Hwang, Hyung-Soo
    • Proceedings of the KIEE Conference
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    • 2006.07d
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    • pp.2097-2098
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    • 2006
  • In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

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Heuristics for Non-Identical Parallel Machine Scheduling with Sequence Dependent Setup Times (작업순서 의존형 준비시간을 갖는 이종병렬기계의 휴리스틱 일정계획)

  • Koh, Shiegheun;Mahardini, Karunia A.
    • Journal of Korean Institute of Industrial Engineers
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    • v.40 no.3
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    • pp.305-312
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    • 2014
  • This research deals with a problem that minimizes makespan in a non-identical parallel machine system with sequence and machine dependent setup times and machine dependent processing times. We first present a new mixed integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is large, we propose four heuristic algorithms including genetic algorithm based heuristics to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithms, we conduct a computational experiment, from which we found the heuristic algorithms show different performances as the problem characteristics are changed and the simple heuristics show better performances than genetic algorithm based heuristics for the case when the numbers of jobs and/or machines are large.

Non-Identical Parallel Machine Scheduling with Sequence and Machine Dependent Setup Times Using Meta-Heuristic Algorithms

  • Joo, Cheol-Min;Kim, Byung-Soo
    • Industrial Engineering and Management Systems
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    • v.11 no.1
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    • pp.114-122
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    • 2012
  • This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.