• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.49-56
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• 2004

Load imbalance is a serious impediment to achieving good performance in parallel processing. Global load balancing schemes cannot adequately manage to balance parallel tasks generated from a single application. Dynamic loop scheduling methods are known to be useful in balancing parallel loops on shared-memory multiprocessor machines. However, their centralized nature causes a bottleneck for the relatively small number of processors in a network of workstations because of order-of-magniture differences in communication overheads. Moreover, improvements of basis loops scheduling methods have not effectively dealt with irregularly distributed workloads in parallel loops, which commonly occur in applications for a network of workstation. In this paper, we present a new reconfigurable and decentralized balancing method for parallel loops on a network of workstations. Since our method supplements performance balancing with those tranditional load balancing methods, it minimizes the overall execution time.

• Smart Media Journal
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• v.11 no.9
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• pp.39-46
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• 2022

The WK-recursive network proposed by Vecchia and Sanges[1] is widely used in the design and implementation of local area networks and parallel processing architectures. It provides a high degree of regularity and scalability, which conform well to a design and realization of distributed systems involving a large number of computing elements. In this paper, the routing of a message is investigated on the WK-recursive network, which is key to the performance of this network. We present an efficient shortest path algorithm on the WK-recursive network, which is simpler than Chen and Duh[2] in terms of design complexity.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.547-549
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• 2007

Recently with increasing the use of parallel computing and cluster system which was connected high speed network, the interest about distributed and parallel file system is increasing. Specially, there are many researches, which focused on optimizing the performance of distributed and parallel file system for the more efficient use of cluster system. In this paper, we analyzed the performance of PVFS(Parallel Virtual File System) in small cluster system. In addition, to improve the PVFS performance we proposed the chancing the size of flow buffer according to the network speed and we optimized the PVFS performance on small cluster system.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.649-650
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• 2006

In this paper, finite element analysis (FEA)-based optimization using Internet distributed computing is proposed for the real world and complex optimization such as optimal design of permanent magnet do motor (PMDCM).

• Korean Journal of Computational Design and Engineering
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• v.13 no.3
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• pp.217-226
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• 2008

Web Services for CAD (WSC) aims at interoperation with CAD systems based on Web Services. This paper introduces one part of WSC which enables remote users to retrieve assembly model data using Web Services. However, retrieving assembly model data takes long time. To resolve this problem, this paper proposes using parallel Web Services. As assembly models comprise a set of part models, it is easy to separate the problem domain into smaller problems. In addition, Web Services inherently supports distributed computing. This characteristic makes the parallel processing of Web Services easy. Firstly, the implementation of WSC which retrieves assembly model data based parallel Web Services is shown. And then, for the comparison, the experiments on the retrieval of assembly model data based on single Web Services and parallel Web Services are shown.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.706-717
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• 2015

The computer generated hologram (CGH) method is the technology which can generate a hologram by using only a personal computer (PC) commonly used. However, the CGH method requires a huge amount of calculational time for the 3D object with a super multi-light source or a high-definition hologram. Hence, some solutions are obviously necessary for reducing the computational complexity of a CGH algorithm or increasing the computing performance of hardware. In this paper, we propose a method which can generate a digital hologram of the 3D object with a super multi-light source using parallel distributed computing. The traditional methods has the limitation of improving CGH performance by using a single PC. However, the proposed method where a server PC efficiently uses the computing power of client PCs can quickly calculate the CGH method for 3D object with super multi-light source. In the experimental result, we verified that the proposed method can generate the digital hologram with 1,5361,536 resolution size of 3D object with 157,771 light source in 121 ms. In addition, in the proposed method, we verify that the proposed method can reduce generation time of a digital hologram in proportion to the number of client PCs.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.13-18
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• 2010

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a novel approach to hybrid optimization algorithm called MGA in MPI (Message Passing Interface) environments. The proposed MGA combines the benefit of rapid convergence property of Mean Field Annealing and the effective genetic operations. This paper also proposes the efficient data structures for pattern related information.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1107-1116
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• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.