• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.630-632
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• 2000

과도한 파일 입출력이 요구되는 병렬파일 시스템의 성능을 결정하는 중요한 요소로서 캐슁과 선반입을 들 수 있다. 본 논문은 캐쉬의 크기에 비해 상대적으로 큰 파일을 요청하는 경우에 시스템 성능에 막대한 영향을 미치는 선반입에 대해서 선반입할 데이터를 결정하는 알고리즘으로 테이블 비교법을 제안하고, 이와 더불어 예측된 데이터의 선반입 여부와 선반입 시기를 결정하는 경우 현재의 가용 입출력 대역폭을 고려하는 기법을 제안한다. 제안하는 선반입 알고리즘을 시뮬레이션을 통하여 기타 선반입 알고리즘과 비교해 본 결과 파일 시스템 성능이 향상되었음을 보여준다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.746-749
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• 2003

인터넷 사용자의 증가와 함께 e-mail 사용자 또한 증가하게 되면서 대량의 메일을 송수신하는 경우, 메일에 대한 효율적 관리의 중요성이 부각되고 있다. 본 논문에서는 수신된 메일의 내용을 텍스트 형태로 가져온 뒤, parallel regular expression을 이용하여 Keyword를 검색하고 추출하여 메일에 첨부되어온 멀티미디어 파일들을 자동으로 분류·저장하는 멀티미디어 첨부파일 자동분류 시스템을 제안하였다. 수신된 메일을 일일이 확인하고 분류해야만 했던 기존의 시스템과는 달리 본 논문에서 제안하고자 하는 시스템을 이용했을 경우 노력과 시간을 절감하고 첨부파일들을 효과적으로 관리할 수 있다는 장점이 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.621-624
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• 2008

많은 파일 시스템은 각 구성 요소로부터 야기되는 다양한 오류에 노출되어 있어서 다양한 복구 기법이 제시되고 있다. 그러나 사용자의 실수로 인한 데이터 손실에 대해서는 상대적으로 대비책이 미비하다. 파일시스템 버저닝(virsioning)은 사용자의 부주의로 인한 데이터 손실을 최소화 할 수 있는 기술이다. 본 연구에서는 단일 시스템을 대상으로 개발된 버저닝 파일시스템인 ext3cow을 병렬 파일시스템인 PVFS2에 적용하여 병렬 파일시스템에서의 버저닝 기술의 적용 가능성을 살펴보았다.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.765-774
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• 2001

In this paper, we proposed the striping policy for the storage nodes in the Linux-based parallel media stream server. We newly developed a storage clustering architecture, and named it as a system RAID architecture. In this system, many storage cluster nodes are grouped to operate as a single server. This system uses unique striping policy to distribute multimedia files into the parallel storage nodes. If a service request occurs, each storage cluster node transmits striped files concurrently to the clients. This scheme can provide the fair distribution of the preprocessing load in all storage cluster nodes. The feature of this system is a relative striping policy based on the file types, service types, and the number of storage nodes to provide the best service.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.4 s.109
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• pp.355-365
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• 2006

The coupled time-integration method with a staggered algorithm based on computational structural dynamics (CSD), finite element method (FEM) and computational fluid dynamics (CFD) has been developed in order to demonstrate physical vibration phenomena due to dynamic aeroelastic excitations. Virtual flutter tests for the spanwise curved ing model have been effectively conducted using the present advanced computational method with high speed parallel processing technique. In addition, the present system can simultaneously give a recorded data file to generate virtual animation for the flutter safety test. The results for virtual flutter test are compared with the experimental data of wind tunnel test. It is shown from the results that the effect of spanwise curvature have a tendency to decrease the flutter dynamic pressure for the same flight condition.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.766-770
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• 2009

Recently, users of multimedia files demand a high capacity file via internet. However, it is difficult to guarantee QoS for high capacity files on internet because of its inconstant bandwidth. For guaranteeing the QoS, CDN (Content Delivery Network) is generally used for contents delivery service. Based on CDN, we propose Content Distribution and Management using Metadata (CDM) system which provides advanced transmission method and searching method. To enhance the transmission rate, CDM system supports segment-unit-based transmission method that enables parallel transmission. Also, we propose a distribution method through content based search.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.372-380
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• 2022

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.7
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• pp.209-216
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• 2023

Modern HPC systems are equipped with many-core CPUs with dozens of cores. When performing parallel I/O in such a system, there is a limit to scalability due to the problem of the LRU lock management policy of the Linux system. The study proposes an improved FinerLRU to solve this problem. Our new FinerLRU improves the parallel write performance of file systems using the buffer cache through granular lock management by increasing the number of LRU locks upto the maximum number of cores. The proposed method was implemented in Linux 5.18.11, and the performance was measured on two types of CPUs, Intel Icelake Xeon and Intel Knights landing, with different characteristics, and it was found that a performance improvement of about two times can be obtained in both types of systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.50-53
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• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.113-114
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• 2009

클러스터 시스템의 응용 분야가 다양화되고 복잡해짐에 따라, 대규모 클러스터 시스템을 보다 효율적으로 사용하기 위해서 실제 사용자의 이용 패턴을 예측할 수 있는 워크로드 분석의 필요성이 높아지고 있다. 이에 본 논문에서는 현재 가동중인 188개의 계산 노드, 3008개 CPU 자원을 보유한 대규모 클러스터 시스템에서 병렬 분산 파일 시스템에 대한 워크로드를 분석하였다.