• Journal of computational fluids engineering
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• v.10 no.1
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• pp.67-72
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• 2005

This research is based on the iterative space-marching method for incompressible and compressible Navier-Stokes equations[1-4]. A principle of parallel computational schemes construction for steady and unsteady problems is suggested. It is analytically proven that convergence of these schemes is unconditional for incompressible case. When the parallel scheme is used the total volume of computations is the sum of a large number of independent and equal parts. Estimation of the speed-up K shows that K > 1000 in ideal case. First results of using the parallel schemes are presented.

• Ocean and Polar Research
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• v.23 no.2
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• pp.173-179
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• 2001

Parallel supercomputing is now a must for oceanographic numerical modelers. Most of today's parallel numerical schemes use synchronous algorithms, where some processors that have finished their tasks earlier than others must wait at synchronization points for correct computation. Hence, the load balancing is a crucial factor, however, it is, in general, difficult to achieve on heterogeneous workstation clusters. We devise an asynchronous algorithm that reduces the idle times of faster processors, and discuss application of the algorithm to some grid problems and implementation on a workstation cluster using Message Passing Interface (MPI).

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM 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.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.567-575
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• 2001

A methodology is developed for designing the minimum-cost water distribution network. The method is based on network simulations and an optimization scheme using genetic algorithms. Being a stochastic optimization scheme, genetic algorithms have advantages over the conventional search algorithms in solving network problems known for their nonlinearities and herculean computational costs. While existing methods focus on the design of either entirely new or parallel augmentation of network systems, the proposed method can be applied to problems having both new branches of tree-type and paralle augmentation in loops. The applicability of the method was shown through a case study for Baekryeon water supply system. The optimized design resulted in the maximum 5.37% savings compared to the conventional design without optimization, while meeting the hydraulic constraints.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.8
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• pp.486-494
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• 2006

In this paper, we introduce a new solution for the load balancing problem, an important issue in parallel processing. Our heuristic load balancing technique called MGA effectively combines the benefit of both mean-field annealing (MFA) and genetic algorithms (GA). We compare the proposed MGA algorithm with other mapping algorithms (MFA, GA-l, and GA-2). A multiprocessor mapping algorithm simulation has been developed to measure performance improvement ratio of these algorithms. Our experimental results show that our new technique, the composition of heuristic mapping methods improves performance over the conventional ones, in terms of solution quality with a longer run time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1655-1662
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• 1993

In this paper we discuss three algorithms-Direct, Successive, and Recursive-on parallel CRC(Cyclic Redundancy Check) verification. The algorithms are derived by combining the byte-syndromes precomputed from the generator polynomial. These algorithms are compared in terms of the amount of hardware and the speed of operation. Since the algorithms can be generalized easily, we took the ATM cell delineation example for easier description. As an application of the algorithm Recursive, an ATM cell delineation module suitable for STM-1 transmission has been successfully realized through commercially available field programmable gate arrays.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2050-2059
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• 1999

Multiprocessor scheduling problem has been an important research area for the past decades. The problem is defined as finding an optimal schedule which minimizes the parallel execution time of an application on a target multiprocessor system. Duplication Based Scheduling (DBS) is a relatively new approach for solving multiprocessor scheduling problems. This paper classifies DBS algorithms into two categories according to the task duplication method used. The paper then presents a new DBS algorithm that extracts the strong features of the two categories of DBS algorithms. The simulation study shows that the proposed algorithm achieves considerable performance improvement over existing DBS algorithms with similar time complexity.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.175-182
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• 2021

Deadlock is a situation in which two or more processes competing for resources are waiting for the others to finish, and neither ever does. There are two different forms of systems, multi-unit and single-unit resource systems. The difference is the number of instances (or units) of each type of resource. Deadlock problem can be modeled as a constrained combinatorial problem that seeks to find a possible scheduling for the processes through which the system can avoid entering a deadlock state. To solve deadlock problem, several algorithms and techniques have been introduced, but the use of metaheuristics is one of the powerful methods to solve it. Genetic algorithms have been effective in solving many optimization issues, including deadlock Problem. In this paper, an improved parallel framework of the genetic algorithm is introduced and adapted effectively and efficiently to deadlock problem. The proposed modified method is implemented in java and tested on a specific dataset. The experiment shows that proposed approach can produce optimal solutions in terms of burst time and the number of feasible solutions in each advanced generation. Further, the proposed approach enables all types of crossovers to work with high performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.843-859
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• 2024

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.1-12
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• 1993

A multiprocessor system for high-speed processing of hybrid image coding algorithms such as H.261, MPEG, or Digital HDTV is presented in this study. Using a combination of highly parallel 32-bit microprocessor, DCT(Discrete Cosine Transform), and motion detection processor, a new processing module is designed for the implementation of high performance coding system. The sysyem is implemented to allow parallel processing since a single module alone cannot perform hybrid coding algorithms at high speed, and crossbar switch is used to realize various parallel processing architectures by altering interconnections between processing modules within the system.