• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.739-740
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• 2009

• Journal of Internet Computing and Services
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• v.4 no.5
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• pp.51-60
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• 2003

Asynchronous iteration is a way to reduce performance degradation of some parallel algorithms due to load imbalance or transmission delay between computing nodes, which requires asymmetric communication between the nodes of different speeds. To implement such asynchronous communication on distributed memory systems, we suggest an MPMD method that creates an additional separate server process on each computing node, and compare it with an SPMD method that creates a single process per node.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.48-57
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• 2009

This paper presents optimal design of direct-driven PM wind generator using MADS (Mesh Adaptive Direct Search). Optimal design of the direct-driven PM Wind Generator, combined with MADS and FEM (Finite Element Method), has been performed to maximize the Annual Energy Production (AEP) over the whole wind speed characterized by the statistical model of the wind speed distribution. In particular, the newly applied MADS contributes to reducing the computation time when compared with Genetic Algorithm (GA) implemented with the parallel computing method.

• Journal of Internet Computing and Services
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• v.1 no.2
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• pp.69-88
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• 2000

One of the most important factors on the performance of the distributed systems is the effective distribution of the software components, There have been a lot of researches on partitioning and allocating the task-based system, while the studies on the allocating the objects of the object-oriented system into the distributed object environments are very little relatively. In this paper. we defines the graph model for partitioning the existing C++ application and allocating the C++ objects into CORBA-base distributed system, In addition, we propose a distributed object allocation algorithm based on this graph model. The performance of distributed systems is determined by the concurrency between objects, the load balance among the processors and the communication cost on the networks. To search for the solutions optimizing the above three factors simultaneously, the object allocation algorithm of this paper is based on the Niched Pareto Genetic Algorithm (NPGA). We performed the experiment on the typical C++ application and CORBA system to prove the effectiveness of our graph model and our object allocation algorithm.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.989-998
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• 2013

We cannot analyze big data, which attracts recent attentions in industry and academy, by batch processing algorithms developed in data mining because big data, by definition, cannot be uploaded and processed in the memory of a single system. So an imminent issue is to develop various leaning algorithms so that they can be applied to big data. In this paper, we review various algorithms for support vector machines in the literature. Particularly, we introduce online type and parallel processing algorithms that are expected to be useful in big data classifications and compare the strengths, the weaknesses and the performances of those algorithms through simulations for linear classification.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2311-2319
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• 1999

We present two heuristic algorithms for the task allocation problem (NP-complete problem) in parallel computing. The problem is to find an optimal mapping of multiple communicating tasks of a parallel program onto the multiple processing nodes of a distributed-memory multicomputer. The purpose of mapping these tasks into the nodes of the target architecture is the minimization of parallel execution time without sacrificing solution quality. Many heuristic approaches have been employed to obtain satisfactory mapping. Our heuristics are based on genetic algorithms and simulated annealing. We formulate an objective function as a total computational cost for a mapping configuration, and evaluate the performance of our heuristic algorithms. We compare the quality of solutions and times derived by the random, greedy, genetic, and annealing algorithms. Our experimental findings from a simulation study of the allocation algorithms are presented.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.67-72
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• 2004

This paper proposes a method improving the convergence speed of optimal solution for parallel genetic algorithm in the network based client-server model. Unlike the existing methods of obtaining global elite only by evaluating local elites in server, the proposed method obtains it by evaluating local elites and improving its fitness by applying genetic algorithm during idle time of the server. By using the improved chromosome in server for the client's genetic algorithm processing, the convergence speed of the optimal solution is increased. The improvement of fitness at the server during the interval of chromosome migration is (equation omitted)(F$_{max}$(g)-F$_{max}$(g-1)), whole F$_{max}$(g) is a max fitness of the g-th generation and G is the number of improved generation by the server. As the number of clients increases and G decreases, the improvement of fitness goes down. However the improvement of fitness is better than existing methods..

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.15-24
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• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.5_6
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• pp.258-264
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• 2003

VLIW architecture has emerged as one of the most effective architectures in dealing with multimedia applications. In multimedia applications, there is ample potential for parallelizing the execution of multiple operations because such applications typically have data intensive processing which often has limited data and/or control dependencies. As the degree of instruction-level parallelism increases, non-clustered VLIW architectures scale poorly because of the tremendous register port pressure. Therefore, clustered VLIW architecture is definitely preferred over non-clustered VLIW architecture when a higher degree of parallelizing is possible as in the case of multimedia processing However, having multiple clusters in an architecture implies that the amount of hardware is quite large, and therefore, power consumption becomes a very crucial issue. In this paper, we propose an algorithm to minimize the peak power consumption without incurring little or no delay penalty. The effectiveness of our algorithm has been verified by various sets of experiments, and up to 30.7% reduction in the peak power consumption is observed compared with the results that is optimized to minimize resources only.