• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.473-483
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• 1999

We introduce a way to construct a supercomputing capable system with some networked PCs, running the Linux operating system and computing power comparable with expensive commercial workstations, and with the Parallel Virtual Machine (PVM) software which enables one to control the total CPUs and memories of the networked PCs. By benchmarking the system using a PVM parallel program, we find that the system's parallel efficiency is close to 90 %.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1150-1156
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• 2008

This paper introduces the PC clustering of the SIMD structure for a distributed processing of on-line contingency to assess a static security of a power system. To execute on-line contingency analysis of a large-scale power system, we need to use high-speed execution device. Therefore, we constructed PC-cluster system using PC clustering method of the SIMD structure and applied to a power system, which relatively shows high quality on the high-speed execution and has a low price. SIMD(single instruction stream, multiple data stream) is a structure that processes are controlled by one signal. The PC cluster system is consisting of 8 PCs. Each PC employs the 2 GHz Pentium 4 CPU and is connected with the others through ethernet switch based fast ethernet. Also, we consider N-1 line contingency that have high potentiality of occurrence realistically. We propose the distributed process algorithm of the SIMD structure for reducing too much execution time on the on-line N-1 line contingency analysis in the large-scale power system. And we have verified a usefulness of the proposed algorithm and the constructed PC cluster system through IEEE 39 and 118 bus system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.445-452
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• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.24-26
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• 2003

본 논문에서는 경험적 최적화 알고리즘인 타부 탐색법을 이용한 배전계통 선로 재구성 시스템을 개발하였다. 제안한 알고리즘은 PC Clustering으로 병렬 처리하여 배전계통 선로 재구성 문제의 최적해 탐색에 소요되는 계산시간을 단축하고, 배전자동화 시스템의 성능개선을 도모하고자 한다. 개발한 PC Cluster System은 이용자의 편의를 위해서 MS Windows환경에서 구축하였고, Visual C++환경에서 개발하였다. 제안한 방법의 유용성을 입증하기 위해 참고 문헌의 예제 계통에 적용한 후 종래의 방법에 의해 구한 재구성 방안과 비교함으로써 제안한 방법의 유용성을 입증하였다.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.129-139
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• 2003

The PC cluster architecture is considered as a cost-effective alternative to the existing supercomputers for realizing a high-performance information retrieval (IR) system. To implement an efficient IR system on a PC cluster, it is essential to achieve maximum parallelism by having the data appropriately distributed to the local hard disks of the PCs in such a way that the disk I/O and the subsequent computation are distributed as evenly as possible to all the PCs. If the terms in the inverted index file can be classified to closely related clusters, the parallelism can be maximized by distributing them to the PCs in an interleaved manner. One of the goals of this research is the development of methods for automatically clustering the terms based on the likelihood of the terms' co-occurrence in the same query. Also, in this paper, we propose a method for duplicate distribution of inverted index records among the PCs to achieve fault-tolerance as well as dynamic load balancing. Experiments with a large corpus revealed the efficiency and effectiveness of our method.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.123-132
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• 1999

With the rapid increases of the amount of data and computing, the parallelization of the computing algorithm becomes necessary more than ever. However the parallelization had been conducted mostly in a super-computer until the rod 1990s, it was not for the general users due to the high price, the complexity of usage, and etc. A new concept for the parallel processing has been emerged in the form of K-clustering form the late 1990s, it becomes an excellent alternative for the applications need high computer power with a relative low cost although the installation and the usage are still difficult to the general users. The mapping algorithms (cut, join, resizing, warping, conversion from raster to vector and vice versa, etc) in GIS are well suited for the parallelization due to the characteristics of the data structure. If those algorithms are manipulated using PC-clustering, the result will be satisfiable in terms of cost and performance since they are processed in real flu with a low cos4 In this paper the tools and the libraries for the parallel processing and PC-clustering we introduced and how those tools and libraries are applied to mapping algorithms in GIS are showed. Parallel programs are developed for the mapping algorithms and the result of the experiments shows that the performance in most algorithms increases almost linearly according to the number of node.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.296-306
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• 2004

LC/MS를 이용하여 펩타이드 혹은 단백질 같은 물질을 분석하는 실험이 급격히 늘어남에 따라 LC/MS 데이터를 자동으로 처리하는 기술에 대한 요구가 커지고 있다. 이러한 LC/MS 데이터의 자동 분석 기술에 대한 연구는 현재 활발히 진행되어 왔고, 이를 직접 구현한 여러 상용 소프트웨어들이 개발되어 있는 상태이다. LC/MS 데이터는 noise 제거, background 데이터 제거, deconvolution 알고리즘을 적용한 분자량(molecular weight) 할당 등의 작업을 거쳐 분석하게 된다. 이러한 과정을 거쳐 얻어진 분자량에 대한 데이터가 올바른 값인지 검증하는 작업이 필요하다. 본 논문에서는 이러한 검증 작업과 관련하여 Peak Clustering and Fitting(이하 PC&F)에 대한 알고리즘을 제안한다. PC&F은 peak 데이터들이 지니고 있는 속성에 대한 Mahalanobis distance를 이용하여 peak 데이터를 각 retention time에 따라 clustering 분석을 하는 작업이다. 본 논문에서 제안하는 PC&F 알고리즘을 Microsoft Visual C++ 6.0 MFC 환경에서 직접 개발한 소프트웨어(PeakClusterFitLCMS)로 실험하였다. 실험결과 PC&F 작업을 통해 동일한 구성물질로부터 발생한 peak 데이터를 모아서 보다 신뢰할 수 있는 분자량을 구할 수 있었고, 구성물질에 의해 발생되지 않은 noise peak 데이터를 찾아 제거시킬 수 있음을 확인할 수 있었다.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1837-1838
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• 2010

기존 hierarchical clustering 은 Time complexity 와 space complexity 가 Large data set 을 clustering 하기에는 적당하지 못하며 이것을 일반 PC 의 메모리 내에서 해결하는데 어려움이 있다. 따라서 본 연구에서는 이러한 어려움을 극복하기 위해 기존 Hierarchical clustering 중 Centroid Linkage 에 새로운 Algorithm 을 제안하여 보다 적은 메모리를 사용하고 빠르게 처리하는 방법을 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.121-124
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• 2003

계산량이 매우 큰 작업의 경우는 단일 프로세서를 이용할 경우 많은 계산 시간이 소요된다 이러한 문제점을 극복하고자 저비용 고효율의 PC Clustering 기법을 사용하면 비용적인 절감의 효과를 얻을 수 있다. 본 논문은 PC Clustering을 이용한 병렬처리를 수행함으로써 시간의 단축을 도모하되 표준 MPI 함수 중 Collective Communication을 취급하는 함수들의 성능을 향상시켜 개선하고 그 성능을 측정하는데 목적이 있다. 또한 표준 MPI 함수를 사용하는 MPICH와 표준 MPI 함수 중 Collective Communication을 사용하는 함수들의 데이터를 압축하여 전송하도록 MPI를 개선하였다. 실험은 윈도우 2000을 탑재한 20개의 노드를 가지는 시스템을 이용하였다. 본 실험의 견과로써 데이터의 양과 노드 수를 증가시킬수록 압축 MPI의 성능이 표준 MPI의 성능을 능가함을 확인할 수 있었다.