• 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.

• Spatial Information Research
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• 제17권3호
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• pp.371-379
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• 2009

최근 그래픽 프로세서(GPU)의 발전에 따라 대량의 프로세서를 탑재한 고성능 그래픽 카드가 개인 컴퓨터에서 널리 사용되고 있다. GPU를 사용하여 CPU의 부하를 줄이면서도 성능을 향상시킬 수 있어서 복잡한 연산을 처리해야 하는 다양한 응용 프로그램에 적용하는 연구가 활발히 진행되고 있다. 본 논문에서는 복잡한 연산이 필요한 공간 데이터 처리의 성능을 향상시키기 위하여 GPU의 병렬 처리 기술을 활용하는 방법을 제안하였다. 원본 공간 데이터를 화면에 출력하기 위해서는 그래픽 처리 연산이 필요하며 같은 종류의 연산을 모든 데이터에 적용해야 하므로 GPU의 SIMD 병렬 처리를 사용하여 성능을 향상시킬 수 있다.

• 디지털산업정보학회논문지
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• 제7권2호
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• pp.59-65
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• 2011

All-optical signal processing is expected to offer advantages in speed and power consumption against over electronics signal processing. It has a potential to solve the bottleneck issues of ultra-high speed communication network nodes. All-optical serial-to-parallel and parallel-to-serial data converters would make it possible to easily process the serial data information of a high-speed optical packet without optical-to-electronic-to-optical data conversion. In this paper, we explain the principle of simple and easily expandable all-optical serial-to-parallel and parallel-to-serial data converters based on Mach-Zehnder interferometers. We experimentally demonstrate these data converters at 10Gbit/s serial data rate. They are useful all-optical devices for the all-optical implementations of label decoding, self-routing, control of variable packets, bit-wise logical operation, and data format conversion.

• 한국통신학회논문지
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• 제26권11A호
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• pp.1963-1970
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• 2001

It is essential design process to analyze processing method and set out top level HW configuration using main parameters before implementation of the SAR processor. This paper identifies the impact of the I/O and algorithm structure upon the parallel processing to be assessed and suggests the practical mapping method fur parallel processing to the SAR data. Also, simulation is performed to the E-SAR processor to examine the usefulness of the method, and the results are analyzed and discussed.

• 아동학회지
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• 제20권3호
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• pp.231-244
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• 1999

This study examined the effects of short and long term playing of video gamer on children's visual parallel processing. All of the 64 fourth grade subjects were above average in IQ. They were classified into high and low video game users. Instruments were a visual parallel processing task consisting of imagery integration items, computers, and the arcade video game, Pac-Man. Subjects were pre-tested with a visual parallel processing task. After one week, the experimental group played video games for 15 minutes, but the control group didn't play. Immediately following this, all children were post-tested by the same task used on the pretest. The data was analyzed by ANCOVA and repeated measures ANOVA. The results showed that relaying short-term video games improved visual parallel processing and that long term experience with video games also affected visual parallel processing. there were no differences between high and low users in visual parallel processing after playing short term video games.

• 대한원격탐사학회지
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• 제28권6호
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• pp.683-691
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• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• 한국정보기술학회 영문논문지
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• 제9권2호
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• pp.89-97
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• 2019

Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.

• 한국정보처리학회논문지
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• 제7권9호
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• pp.2827-2835
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• 2000

최근, 멀티미디어 정보처리와 같은 대규모 데이터 처리에 필수적인 입출력 시스템의 성능을 높이기 위하여 많은 관심이 집중되고 있으며, 고성능 병렬화일 시스템에 관한 연구도 이런 노력에 속한다. 본 연구에서는 고성능 병렬화일 시스템을 위한 효율적인 디스크 할당 방법을 제안한다. 즉, 병렬화일의 자료 분산(data declustering)특성을 이용하여 병렬화일에 대한 병렬도 개념을 정의하고, 이를 기반으로 여러 병렬화일들이 동시에 처리되는 경우에, 최대의 작업처리량(throughput)을 얻기 위한 각 병렬화일에 적합한 디스크상의 자료 분산 정도를 계산하는 방법을 제안한다. 또한 동시에 처리되는 병렬화일들이 많이 늘어날수록, 최대의 작업처리량을 얻기 위한 계산이 너무 복잡해지므로, 효율적인 근사 디스크 할당 알고리즘도 아울러 제안한다. 제안된 근사 알고리즘은 계산이 간단하고, 특히 입출력 작업부하(workload)가 높은 환경에서는 매우 효율적임을 보여준다. 또한 입출력 요구들의 도착 비율이 무한대일 경우, 근사 알고리즘만을 이용하여도 최대 작업처리량을 위한 최적 디스크 할당을 얻을 수 있음을 증명하였다.

• 한국항행학회논문지
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• 제25권4호
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• pp.293-298
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• 2021

일반적으로 유도무기의 탐색기와 유도조종장치는 유도탄의 상태를 나타내기 위해 표적, 탐색, 인지, 포착정보를 처리하여 유도무기의 운용 및 제어를 담당하는 역할을 한다. 유도에 필요한 신호는 시선 변화율 신호, 시각 신호, 종말 단계 동체 지향 신호이며, 발사 통제에 필요한 신호는 표적, 감지 신호가 필요하다. 최근 유도탄의 복잡하고 처리하기 어려운 유도탄 신호를 실시간으로 처리하기 위해 유도탄의 데이터 처리 속도를 높여야 한다. 본 연구는 PLINQ(Parallel Language-Integrated Query)의 병렬 알고리즘 방법 중 스톱앤고와 역 열거형 알고리즘을 적용한 후 유도탄 점검 프로그램을 이용하여 실시간으로 유도탄 필요 신호 데이터 처리속도를 비교 후 처리결과를 나타내었다. 도출된 데이터 처리결과 기준으로 다중코어 처리방식과 단독코어 처리방식 CPU(Central Processing Unit) 처리속도 비교, CPU 코어 이용률을 비교하고 병렬처리 알고리즘 적용 시 유도탄 데이터 처리에 효과적 방법을 제안한다.

• Journal of Information Processing Systems
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• 제13권3호
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• pp.518-532
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• 2017

Due to the rapid growth of the amount of data, research on bigdata processing has been highlighted. For bigdata processing, CUBRID Shard is able to support query processing in parallel way by dividing the database into a number of CUBRID servers. However, CUBRID Shard can answer a user's query only when the query is required to gain accesses to a single CUBRID server, instead of multiple ones. To solve the problem, in this paper we propose a CUBRID based distributed parallel query processing system that can answer a user's query in parallel and distributed manner. Finally, through the performance evaluation, we show that our proposed system provides 2-3 times better performance on query processing time than the existing CUBRID Shard.