• 한국전산유체공학회:학술대회논문집
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• 2001.05a
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• pp.161-169
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• 2001

At the end of 1999, the TeraCluster Project in the KISTI Supercomputing Center was initiated to explore the possibility of PC clusters as a scientific computing platform to replace the Cray T3E system in KISTI by 2002. Since actual performance of a computing system varies significantly for different architectures, representative in-house codes from major application fields were executed to evaluate the actual performance of systems with different combination of CPU, network and network topology. As an example of practical CFD(Computational Fluid Dynamics) simulations, the flow past the Onera-M6 wing and the flow past a infinite wing were simulated on a clusters of Linux and several other hardware environments.

• Structural Engineering and Mechanics
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• v.38 no.4
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• pp.405-416
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• 2011

An efficient two-level domain decomposition parallel algorithm is suggested to solve large-DOF structural problems with nonlinear material models generating unsymmetric tangent matrices, such as a group of plastic-damage material models. The parallel version of the stabilized bi-conjugate gradient method is developed to solve unsymmetric coarse problems iteratively. In the present approach the coarse DOF system is solved parallelly on each processor rather than the whole system equation to minimize the data communication between processors, which is appropriate to maintain the computing performance on a non-supercomputer level cluster system. The performance test results show that the suggested algorithm provides scalability on computing performance and an efficient approach to solve large-DOF nonlinear structural problems on a cluster system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.55-58
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• 2011

This paper proposes a method of occluded number recognition by matching interest points. Interest points of input pattern are found via SURF features extracting and matched to interest points of clusters in database following three steps: SURF matching, coordinate matching and SURF matching on coordinate matched points. Then the satisfied interest points are counted to compute matching rate of each cluster. The input pattern will be assigned to cluster having highest matching rate. We have experimented our method to different numerical fonts and got encouraging results.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.195-210
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• 2007

Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.10
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• pp.550-560
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• 2002

External sort on cluster computers requires not only fast internal sorting computation but also careful scheduling of disk input and output and interprocessor communication through networks. This is because the overall time for the execution is determined by reflecting the times for all the jobs involved, and the portion for interprocessor communication and disk I/O operations is significant. In this paper, we improve the sorting performance (sorting throughput) on a cluster of PCs with a low-speed network by developing a new algorithm that enables even distribution of load among processors, and optimizes the disk read and write operations with other computation/communication activities during the sort. Experimental results support the effectiveness of the algorithm. We observe the algorithm reduces the sort time by 45% compared to the previous NOW-sort[1], and provides more scalability in the expansion of the computing nodes of the cluster as well.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1056-1059
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• 2003

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.33-40
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• 2006

A parallel computing strategy for finite element(FE) processing is described and implemented in nonlinear explicit FE code and its parallel performances are evaluated. A self-made linux-cluster supercomputer with 520 CPUs is used as a bench mark test bed. It is observed that speed-up is increased almost idealy even up to 256 CPUs for a large scale model. A communication over head and its effect on the parallel performance is also examined. Parallel performance is compare with the commercial code and developed code shows superior performance as the number of CPUs used are increased.

• ETRI Journal
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• v.29 no.5
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• pp.545-558
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• 2007

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1744-1750
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• 2008

As the computing and network technique move rm and spread widly, the usage of multimedia application becomes in general while the usage of text based application becomes low. Especially the application which treats the streaming media such as video or movie, one of multimedia data, holds a majority in the usage of computing. MPEG, one of the typical compression standard of streaming media, provides very high compression ratio so that general users could be close to the streaming media with easy usage. However, the encoding of MPEG requires lots of computing power and time. In the paper, we design and implement a parallel MPEG encoder with MPI in cluster envrionment to reduce the encoding time of MPEG.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1238-1259
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• 2019

Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference-Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.