• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.586-590
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• 2009

The objective of this paper describes the design of MapReduce over Peer-to-Peer network for dynamic environments applications. MapReduce is a software framework used for Cloud Computing which processing large data sets in a highly-parallel way. Based on the Peer-to-Peer network character which node failures will happen anytime, we focus on using a DHT routing protocol which named Pastry to handle the problem of node failures. Our results are very promising and indicate that the framework could have a wide application in P2P network systems while maintaining good computational efficiency and scalability. We believe that, P2P networks and parallel computing emerge as very hot research and development topics in industry and academia for many years to come.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.119-124
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• 2022

Successful underwater localization requires a large-scale, parallel computing environment that can be mounted on various underwater robots. Accordingly, we propose a design method for an artificial intelligence computing platform for underwater localization. The proposed platform consists of a total of four hardware modules. Transponder and hydrophone modules transmit and receive sound waves, and the FPGA module rapidly pre-processes the transmitted and received sound wave signals in parallel. Jetson module processes artificial intelligence based algorithms. We performed a sound wave transmission/reception experiment for underwater localization according to distance in an actual underwater environment. As a result, the designed platform was verified.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.48-61
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• 2017

An IaaS service of a cloud computing environment makes itself attractive for running large scale parallel application thanks to its innate characteristics that a user can utilize a desired number of high performance virtual machines without maintenance cost. The total execution time of a parallel application on a high performance computing environment depends on a task scheduling algorithm. Most studies on task scheduling algorithms on cloud computing environment try to reduce a user cost, and studies on task scheduling algorithms that try to reduce total execution time are rarely carried out. In this paper, we propose a task scheduling algorithm called an HAGD and a performance enhancement method called a group task duplication method of which the HAGD utilizes. The group task duplication method simplifies previous task duplication method, and the HAGD uses the group task duplication method or a task insertion method according to the characteristics of a computing environment and an application. We found that the proposed algorithm provides superior performance regardless of the characteristics in terms of normalized total execution time through performance evaluations.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.119-131
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• 2018

The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individual experiences in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. The purpose of this paper is to empirically evaluate the efficiency and effectiveness of spatial analysis in cloud computing platforms. We compare the computing speed for calculating the measure of spatial autocorrelation and performing geographically weighted regression analysis between a local machine and spot instances on clouds. The results indicate that there could be significant improvements in terms of computing time when the analysis is performed parallel on clouds.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2320-2331
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• 1999

Over the Internet, in order to utilize a collection of idle computers as a parallel computing platform, we propose a new scheme called GICE(Global Internet Computing Environment). GICE is motivated to obtain high programmability, efficient support for heterogeneous computing resources, system scalability, and finally high performance. The programming model of GICE is based on a single address space. GICE is featured with a Java based programming environment, a dynamic resource management scheme, and efficient parallel task scheduling and execution mechanisms. Based on a prototype implementation of GICE, we address the concept, feasibility, complexity and performance of Internet computing.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.24-26
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• 2012

하드웨어 컴퓨팅 자원의 성능을 최대로 활용하기 위한 소프트웨어 기술로 가상 머신 기술이 활발하게 사용되고 있다. 또, 하드웨어 컴퓨팅 자원의 병렬성을 극대화하기 위한 소프트웨어 기술로 함께 주목 받고 있는 기술이 분산 병렬 프로그래밍 기술이다. 그러나 가상머신에서 데이터를 병렬로 처리할 경우 I/O의 속도 저하 문제 등과 같은 단점이 있다. 본 논문에서는 성능 저하 없이 가상 머신에서 병렬 프로그래밍을 수행할 수 있도록 가상 머신에서의 태스크 개수 결정을 위한 선행 연구로서, 가상 머신 환경을 만들고, 여러 가지 속성 값을 변경하여 MapReduce 성능 평가결과를 보인다. 본 논문에서 수행한 실험의 결과는 가상머신에서의 MapReduce 태스크 결정 방법으로 연구에 참고자료로 사용될 수 있을 것이다.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.275-278
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• 2012

신경망 이론은 그 특성상 각각의 뉴런과 신경들 사이의 병렬적인 처리에 의해 Input에 대한 Output을 계산해 낸다. 하지만, 현대 컴퓨터들은 CPU를 통한 순차처리 방식으로 정보를 취급하기에 그 근본 구조가 달라 병렬구조를 모사하기 위해 계산하는 과정에 많은 시간이 소요된다. 본 논문에서는 신경망 학습을 NVIDIA사에서 제공한 CUDA를 사용하여 병렬 컴퓨팅 구조로 수행함으로서 시간을 단축시키는 것을 확인하고자 한다.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.586-592
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• 2000

터보머신 태부에 존재하는 정익 - 동익의 상호작용 유동현상을 수치모사 하는 코드를 병렬화 하였다 정익 - 동익의 상호작용을 해석하는 데에 편리하도륵 Multi-Block Grid System을 도입하여 계산영역을 형성하였고, 동익의 움직임으로 인해 발생하는 Sliding Interface부분은 Patched 알고리즘을 적용하여 해석하였다. 정익과 동익의 수를 1대 1로 단순화시켜 수치모사한 결과와 정익과 동익의 수를 실제 조건과 더 비슷하게 설정한 3대 4의 비율로 맞추어 수치모사한 결과를 비교하였다. 또한, 병렬컴퓨팅으로 인해 단축된 계산시간을 다른 연구에서의 계산시간들과 서로 비교하였다. 2차원 비정상 압축성 Navier-Stokes 방정식이 이용되었고, 난류모델링에는 K-w SST 모델링이 적응되었다. Roe의 FDS 기법을 사용하여 플럭스를 계산하였고, MUSCL 기법을 적용하여 3차의 공간정확도를 갖도록 하였다. 시간적분에는 이보성의 DP-SGS를 사용하였다. 해석결과의 분석에는 Time-averaged pressure distribution과 Pressure amplitude distribution 데이터를 사용했다.

• Electronics and Telecommunications Trends
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• v.32 no.4
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• pp.48-56
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• 2017

대규모 고성능 컴퓨팅 시스템에서 경량커널은 전통적으로 계산 노드에 탑재되어 특정 연산만을 수행한다. 특히 경량커널은 병렬 프로그램을 실행함에 있어 성능을 최대한 끌어올리기 위하여 자원 간의 간섭을 최소화할 수 있도록 개발되어 사용되고 있다. 최근에는 수천 개의 코어가 장착된 고성능 컴퓨팅 환경은 병렬프로그램뿐만 아니라 일반 응용 및 대규모 분산 응용에서도 필요하다. 고성능 컴퓨팅 환경에서는 매니코어와 메모리 자원이 늘어남에 따라 성능 확장성을 요구하는 현실적인 운영체제의 구조로서 경량커널과 리눅스를 같이 실행하는 멀티커널 구조를 선호하고 있다. 본고에서는 이러한 선행연구를 소개하고 매니코어 시스템에서 활용되는 최근 경량커널의 동향에 대해 살펴본다.