• Journal of Information Processing Systems
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• 제12권1호
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• pp.149-168
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• 2016

The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권3호
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• pp.954-972
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• 2012

Content Delivery Networks (CDN) server loads that fluctuant necessitate CDN to improve its service scalability especially when the peak load exceeds its service capacity. The peer assisted scheme is widely used in improving CDN scalability. However, CDN operators do not want to lose profit by overusing it, which may lead to the CDN resource utilization reduced. Therefore, improving CDN scalability moderately and guarantying CDN resource utilization maximized is necessary. However, when and how to use the peer-assisted scheme to achieve such improvement remains a great challenge. In this paper, we propose a new method called Dynamic Moderate Peer-assisted Method (DMPM), which uses time series analysis to predict and decide when and how many server loads needs to offload. A novel peer-assisted mechanism based on the prediction designed, which can maximize the profit of the CDN operators without influencing scalability. Extensive evaluations based on an actual CDN load traces have shown the effectiveness of DMPM.

• Journal of Communications and Networks
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• 제18권2호
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• pp.218-226
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• 2016

Coordination among users is an inevitable but time-consuming operation in wireless networks. It severely limit the system performance when the data rate is high. We present FC-MAC, a novel MAC protocol that can complete a contention within one contention slot over a joint frequency-code domain. When a node takes part in the contention, it generates randomly a contention vector (CV), which is a binary sequence of length equal to the number of available orthogonal frequency division multiplexing (OFDM) subcarriers. In FC-MAC, different user is assigned with a distinct signature (i.e., PN sequence). A node sends the signature at specific subcarriers and uses the sequence of the ON/OFF states of all subcarriers to indicate the chosen CV. Meanwhile, every node uses the redundant antennas to detect the CVs of other nodes. The node with the minimum CV becomes the winner. The experimental results show that, the collision probability of FC-MAC is as low as 0.05% when the network has 100 nodes. In comparison with IEEE 802.11, contention time is reduced by 50-80% and the throughput gain is up to 200%.

• 전자공학회논문지CI
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• 제41권6호
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• pp.23-37
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• 2004

자바의 쓰레드는 다중 처리 환경에서 하나의 프로그램 공간 내의 독립적인 프로세스로 취급되는 객체 요소이므로 병렬처리를 위한 독립적인 프로세스로 활용할 수 있다. 또한, 자바의 동기화 메커니즘과 쓰레드를 활용하면 병렬 처리를 수행하는 응용프로그램을 쉽게 작성할 수 있다. 이에 따라, 자바의 병렬 처리 지원 기능을 분산된 컴퓨팅 환경에 적용하기 위한 많은 연구 결과가 있다. 본 논문에서는 레거시 자바 프로그램에 포함된 쓰레드를 분산된 컴퓨팅 환경에서 병렬 수행 하도록 지원하는 시스템 환경을 제안한다. TORB(Transparent Object Request Broker)라고 명명된 본 시스템은 프로그래밍 투명성을 지원하므로 이미 작성된 레거시 자바 프로그램을 간단한 변환 과정을 거친 후 병렬 수행 하도록 지원한다. TORB는 본 연구팀에서 이미 발표한 분산 프로그래밍 도구의 기능을 확장한 것이며, 이는 지정된 기능을 지정된 컴퓨터에서 수행하도록 지원하는 전형적인 분산처리 기능만을 보유하고 있었다.

• 한국경영과학회지
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• 제39권4호
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• pp.19-31
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• 2014

We study parallel processing techniques for the R programming language of high performance computing technology. In this study, we used massively parallel computing system which has 25,408 cpu cores. We conducted a performance evaluation of a distributed memory system using MPI and of a the shared memory system using OpenMP. Our findings are summarized as follows. First, For some particular algorithms, parallel processing is about 150 times faster than serial processing in R. Second, the distributed memory system gets faster as the number of nodes increases while shared memory system is limited in the improvement of performance, due to the limit of the number of cpus in a single system.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.194-199
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• 2019

As the performance of the system increases, more parallelized data is being processed than single processing of data. Today's cpu structure has been developed to leverage multicore, and hence data processing methods are being developed to enable parallel processing. In recent years desktop cpu has increased multicore, data is growing exponentially, and there is also a growing need for data processing as artificial intelligence develops. This neural network of artificial intelligence consists of a matrix, making it advantageous for parallel processing. This paper aims to speed up the processing of the system by using raspberrypi to implement the cluster building and parallel processing system against the backdrop of the foregoing discussion. Raspberrypi is a credit card-sized single computer made by the raspberrypi Foundation in England, developed for education in schools and developing countries. It is cheap and easy to get the information you need because many people use it. Distributed processing systems should be supported by programs that connected multiple computers in parallel and operate on a built-in system. RaspberryPi is connected to switchhub, each connected raspberrypi communicates using the internal network, and internally implements parallel processing using the Message Passing Interface (MPI). Parallel processing programs can be programmed in python and can also use C or Fortran. The system was tested for parallel processing as a result of multiplying the two-dimensional arrangement of 10000 size by 0.1. Tests have shown a reduction in computational time and that parallelism can be reduced to the maximum number of cores in the system. The systems in this paper are manufactured on a Linux-based single computer and are thought to require testing on systems in different environments.

• 한국정보처리학회논문지
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• 제4권11호
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• pp.2903-2913
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• 1997

본 논문은 공유 및 분산 메모리 구조를 가진 병렬 컴퓨터의 프로그래밍 환경을 지원하기 위하여 ParaC 언어를 설계하고 구현한 내용을 기술한다. ParaC 언어는 확장성 높은 병렬 컴퓨터의 시스템 자원을 사용자가 효과적으로 이용할 수 있도록 설계되었다. 이것은 C 언어에 공유 메모리 환경을 위한 병렬 구문과 동기화 구문, 그리고 분산 메모리 환경을 위한 원격 태스크 구문을 추가함으로써 이루어졌다. 언어의 구현을 위하여 C 언어로의 번역 방법을 기술하였으며, 이 방법을 사용한 번역기와 확장 구문을 위한 실행시간 라이브러리를 구현하였다.

• 한국CDE학회논문집
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• 제16권3호
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• pp.236-245
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• 2011

Many researchers have recently studied multi-level formulation strategies to solve the MDO problems and they basically distributed the coupling compatibilities across all disciplines, while single-level formulations concentrate all the controls at the system-level. In addition, approximation techniques became remedies for computationally expensive analyses and simulations. This paper studies comparisons of the MDO methods with respect to computing performance considering both conventional sequential and modem distributed/parallel processing environments. The comparisons show Individual Disciplinary Feasible (IDF) formulation is the most efficient for sequential processing and IDF with approximation (IDFa) is the most efficient for parallel processing. Results incorporating to popular design examples show this finding. The author suggests design engineers should firstly choose IDF formulation to solve MDO problems because of its simplicity of implementation and not-bad performance. A single drawback of IDF is requiring more memory for local design variables and coupling variables. Adding cheap memories can save engineers valuable time and effort for complicated multi-level formulations and let them free out of no solution headache of Multi-Disciplinary Analysis (MDA) of the Multi-Disciplinary Feasible (MDF) formulation.

• 대한기계학회논문집B
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• 제21권8호
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• pp.1024-1033
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• 1997

The past several years have witnessed an ever-increasing acceptance and adoption of parallel processing, both for high performance scientific computing as well as for more general purpose applications. Furthermore with increasing needs to perform the complex flow calculations in an efficient manner, the use of the message passing model on distributed networks has emerged as an important alternative to the expensive supercomputers. This work attempts to provide a generic framework to enable the parallelization of all CFD-related works using the master-slave model. This framework consists of (1) input geometry, (2) domain decomposition, (3) grid generation, (4) flow computations, (5) flow visualization, and (6) output display as the sequential components, but performs computations for (2) to (5) in parallel on the workstation clustering. The flow computations are parallized by having multiple copies of the flow-code to solve a PDE on different spatial regions on different processors, while their flow data are exchanged across the region boundaries, and the solution is time-stepped. The Parallel Virtual Machine (PVM) is used for distributed communication in this work.

• 인터넷정보학회논문지
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• 제18권3호
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• pp.1-9
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• 2017

스마트-시티는 스마트-시티의 사물인터넷(Internet of Things: IoT) 디바이스를 비롯한 수많은 인프라를 지능적으로 관리하고, 다양한 스마트 어플리케이션을 도시민에게 제공한다. 스마트-시티에서는 스마트-시티 어플리케이션에서 필요한 다양한 정보를 제공하기 위하여 수많은 사물인터넷 기기들로부터 끊임없이 발생하는 대규모의 스트림 빅-데이터를 지능적으로 처리하는 기능이 필요하다. 하지만, 스마트-시티에서 대규모의 스트림 빅-데이터를 처리하는 것에는 실시간 처리와 관련된 제약들이 존재한다. 본 스마트-시티-사업단에서는 선행 연구에서 스마트-시티미들웨어와 이를 이용한 스트림-리즈닝 방법론 및 시스템을 개발하였다. 스마트-시티에서 스마트 서비스를 제공하기 위하여, 스마트-시티-사업단에서는 스트림-리즈닝을 사용하는 방법론을 사용한다. 이 스트림-리즈닝은 대용량 데이터의 실시간 처리를 필요로 한다. 따라서, 후속연구로서 스마트-시티미들웨어의 클라우드-컴퓨팅 플랫폼을 이용하여 스트림-리즈닝을 위한 실시간 분산병렬처리 클라우드-컴퓨팅 방법론과 시스템을 개발하였다. 본 논문에서는 스마트-시티에서 발생하는 사물인터넷 빅-데이터를 스트림-리즈닝에 사용하기 위하여 이 후속연구에서 개발된 클라우드 기반 실시간 분산병렬처리 연구결과를 소개한다. 스마트-시티의 각종 센서들로부터 전송되어지는 사물인터넷 빅-데이터를 사용하여 스트림-리즈닝하는 데 필요한 클라우드-컴퓨팅 기반의 실시간 분산처리 방법론과 시스템을 소개하고 있으며, 이 방법론을 선행연구에서 개발한 스마트-시티 미들웨어에 구현하여 실시간 분산처리 성능을 평가한 것을 소개한다.