• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.294-297
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• 2007

다양한 스토리지와 파일 시스템들이 시스템의 신뢰도를 증가시키기 위해 스냅샷을 이용하고 있다. 하지만 현재 널리 사용되고 있는 볼륨 단에서의 스냅샷은 스냅샷 이미지를 생성하는데 필요한 시간이 볼륨의 크기에 비례하고 스냅샷 이미지를 생성하는 동안의 '파일시스템 입출력 성능'이 현저하게 저하되는 단점이 있다. 이러한 단점을 극복하기 위해 파일 시스템 단에서의 스냅샷 기법에 대한 연구가 진행되어 왔으며 "snapFS", "Ext3Cow" 그리고 "New version of SnapFS" 등의 파일 시스템들이 개발 되었다. 본 논문에서는 네트워크로 연결된 스토리지 상에서의 데이터 복구를 효율적으로 처리하기 위해 대용량 파일을 처리하는데 적합한 XFS에 스냅샷 기능을 추가한 snap+XFS에 대해 언급한다.

• Journal of KIISE
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• v.43 no.3
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• pp.304-313
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• 2016

We present a novel memory-efficient parallel ray casting algorithm for unstructured grid volume rendering on multi-core CPUs. Our method is based on the Bunyk ray casting algorithm. To solve the high memory overhead problem of the Bunyk algorithm, we allocate a fixed size local buffer for each thread and the local buffers contain information of recently visited faces. The stored information is used by other rays or replaced by other face's information. To improve the utilization of local buffers, we propose an image-plane based ray grouping algorithm that makes ray groups have high coherency. The ray groups are then distributed to computing threads and each thread processes the given groups independently. We also propose a novel hash function that uses the index of faces as keys for calculating the buffer index each face will use to store the information. To see the benefits of our method, we applied it to three unstructured grid datasets with different sizes and measured the performance. We found that our method requires just 6% of the memory space compared with the Bunyk algorithm for storing face information. Also it shows compatible performance with the Bunyk algorithm even though it uses less memory. In addition, our method achieves up to 22% higher performance for a large-scale unstructured grid dataset with less memory than Bunyk algorithm. These results show the robustness and efficiency of our method and it demonstrates that our method is suitable to volume rendering for a large-scale unstructured grid dataset.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.122-124
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• 2000

MIDAS-III는 한국전자통신연구원에서 개발한 바다-DBMS의 저장 시스템이다. MIDAS-III는 대용량 멀티미디어 데이터서버의 저장 시스템으로 가능하도록 3차 저장장치를 부착하여 MIDAS-III/TS로 확장 구현되었다. 3차 저장장치를 관리하기 위하여, MIDAS-III/TS는 3차 저장장치 관리 볼륨을 새롭게 구성하였다. 본 논문에서는 먼저 MIDAS-III/TS의 구조와 확장 전 MIDAS-III의 고장 회복 유틸리티인 restart에 대해 살펴본다. 그리고, MIDAS-III/TS의 고장 회복 유틸리티인 restart-t의 구현을 위하여 MIDAS-III가 3차 저장장치를 부착함에 따라 새롭게 발생할 수 있는 고장의 유형과 고장 회복에 있어서의 문제점에 대해 기술하고 회복 방법을 제시한다.

• Journal of the Korea Computer Graphics Society
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• v.21 no.3
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• pp.36-45
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• 2015

It is important subject of research in engineering and natural science field that creating continuing high-definition image from very large volume data. The necessity of software that helps analyze useful information in data has improved by effectively showing visual image information of high resolution data with visualization technique. In this paper, we designed multi-platform visualization system based on client-server to analyze and express earth environment data effectively constructed with observation and prediction. The visualization server comprised of cluster transfers data to clients through parallel/distributed computing, and the client is developed to be operated in various platform and visualize data. In addition, we aim user-friendly program through multi-touch, sensor and have made realistic simulation image with image-based lighting technique.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.179-186
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• 2013

PACS is a medical system for digital medical images, and PACS expand to web-based service using public network, DICOM files should be protected from the man-in-the-middle attack because they have personal medical record. To solve the problem, we designed flexible secure transmission system using IPSec and adopted to a web-based three-dimensional medical image system. And next, we performed the performance evaluation changing integrity and encryption algorithm using DICOM volume dataset. At that time, combinations of the algorithm was 'DES-MD5', 'DES-SHA1', '3DES-MD5', and '3DES-SHA1, and the experiment was performed on our test-bed. In experimental result, the overall performance was affected by encryption algorithms than integrity algorithms, DES was approximately 50% of throughput degradation and 3DES was about to 65% of throughput degradation. Also when DICOM volume dataset was transmitted using secure transmission system, the network performance degradation had shown because of increased packet overhead. As a result, server and network performance degradation occurs for secure transmission system by ensuring the secure exchange of messages. Thus, if the secure transmission system adopted to the medical images that should be protected, it could solve server performance gradation and compose secure web PACS.

• Journal of the Korea Computer Graphics Society
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• v.26 no.1
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• pp.17-25
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• 2020

We present a novel multi-core CPU based parallel algorithm for the cell-connectivity information extraction algorithm, which is one of the preprocessing steps for volume rendering of unstructured grid data. We first check the synchronization issues when parallelizing the prior serial algorithm naively. Then, we propose a 3-step parallel algorithm that achieves high parallelization efficiency by removing synchronization in each step. Also, our 3-step algorithm improves the cache utilization efficiency by increasing the spatial locality for the duplicated triangle test process, which is the core operation of building cell-connectivity information. We further improve the efficiency of our parallel algorithm by employing a memory pool for each thread. To check the benefit of our approach, we implemented our method on a system consisting of two octa-core CPUs and measured the performance. As a result, our method shows continuous performance improvement as we add threads. Also, it achieves up to 82.9 times higher performance compared with the prior serial algorithm when we use thirty-two threads (sixteen physical cores). These results demonstrate the high parallelization efficiency and high cache utilization efficiency of our method. Also, it validates the suitability of our algorithm for large-scale unstructured data.