• International Journal of Contents
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• v.12 no.2
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• pp.6-11
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• 2016

We developed a pilot-job based large-scale distributed computing system to support HTC and MTC, called HTCaaS (High-Throughput Computing as a Service), which helps scientists solve large-scale scientific problems in areas such as pharmaceutical domains, high-energy physics, nuclear physics and bio science. Since most of these problems involve critical data that affect the national economy and activate basic industries, data privacy is a very important issue. In this paper, we implement a privacy enhanced data security mechanism to support HTC and MTC in a large-scale distributed computing system and show how this technique affects performance in our system. With this mechanism, users can securely store data in our system.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.210-210
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• 2006

IT technologies(Chips, Grid and e-Science) are rapidly changed from 1965. In 1965, Intel co-founder Gordon Mooresaq the future. His prediction popularly known as Moore's law, state that the computer chips double in power every 18 months Grid computing offers a model for solving massive computational problems by making use of the unused resources of large numbers of disparate, often desktop, computers treated as a virtual cluster embedded in a distributed telecommunications infrastructure. In this paper, I will discuss current status of supercomputing technology and haw we can use these on CFD. Functionally, one can classify Grids into several types:

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.193-196
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• 2002

이 논문에서는 인터넷 기반 분산컴퓨팅 테스트베드를 구축하고 이를 활용하여 '단백질과 리간드 결합 세기예측을 통한 신약후보물질탐색'을 수행한 결과를 보인다. 윈도우즈 계열의 임의 자발적 인터넷 사용자 1,217대의 PC를 대상으로 수행되었으며, 시스템이 목표로 하고 있는 확장성, 가용성, 신뢰성, 적응성 등이 충분히 적응성을 보이고, 실험을 통해 얻어진 교훈을 피드백으로 향후 연구 방향을 제시한다.

• Journal of KIISE
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• v.42 no.1
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• pp.15-22
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• 2015

In this paper, we present a case study for a drug repositioning simulation based on distributed supercomputing technology that requires highly efficient processing of large-scale computations. Drug repositioning is the application of known drugs and compounds to new indications (i.e., new diseases), and this process requires efficient processing of a large number of docking tasks with relatively short per-task execution times. This mechanism shows the main characteristics of a Many-Task Computing (MTC) application, and as a representative case of MTC applications, we have applied a drug repositioning simulation in our HTCaaS system which can leverage distributed supercomputing infrastructure, and show that efficient task dispatching, dynamic resource allocation and load balancing, reliability, and seamless integration of multiple computing resources are crucial to support these challenging scientific applications.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.91-96
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• 2013

The application field of supercomputing systems are changing to support into the field for both a large-volume data processing and high-performance computing at the same time such as bio-applications. These applications require high-performance distributed file system for storage management and efficient high-speed processing of large amounts of data that occurs. In this paper, we introduce MAHA-FS for supercomputing systems for processing large amounts of data and high-performance computing, providing excellent metadata operation performance and IO performance. It is shown through performance analysis that MAHA-FS provides excellent performance in terms of the metadata processing and random IO processing.

• Coupled systems mechanics
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• v.8 no.2
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• pp.99-109
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• 2019

This work investigates the accuracy and performance of a $FE^2$ multi-scale implementation used to predict the behavior of composite materials. The equations are formulated assuming the small deformations solid mechanics approach in non-linear material models with hardening plasticity. The uniform strain boundary conditions are applied for the macro-to-micro transitions. A parallel algorithm was implemented in order to solve large engineering problems. The scheme proposed takes advantage of the domain decomposition method at the macro-scale and the coupling between each subdomain with a micro-scale model. The precision of the method is validated with a composite material problem and scalability tests are performed for showing the efficiency.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.547-549
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• 2007

Recently with increasing the use of parallel computing and cluster system which was connected high speed network, the interest about distributed and parallel file system is increasing. Specially, there are many researches, which focused on optimizing the performance of distributed and parallel file system for the more efficient use of cluster system. In this paper, we analyzed the performance of PVFS(Parallel Virtual File System) in small cluster system. In addition, to improve the PVFS performance we proposed the chancing the size of flow buffer according to the network speed and we optimized the PVFS performance on small cluster system.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.1417-1420
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• 2005

고속 네트워크로 연결된 대형 병렬 컴퓨터 및 클러스터 시스템의 사용이 증가되면서, 대용량 스토리지의 효율적인 활용을 위한 분산 및 병렬 파일 시스템에 대한 관심이 증가하고 있다. 특히 다수의 컴퓨터에 장착된 디스크 또는 스토리지를 네트워크로 연결하여 하나의 논리적이 파일 시스템으로 구성하는 분산 및 병렬 파일 시스템은 유휴 자원의 활용, bandwidth 및 throughput의 증대라는 장점으로 많은 연구가 진행 중이다. 본 논문에서는 대표적인 분산 및 병렬 파일 시스템을 대상으로 소규모 클러스터 시스템에서 성능 및 특징을 비교, 분석하였다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.467-470
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• 2011

일반 PC의 메인 메모리에 올릴 수 없는 거대 용량의 데이터의 경우 가시화를 통한 해석을 수행하는데 어려움이 많다. 본 논문에서는 이러한 거대 용량의 데이터를 실시간으로 처리하기 위한 분산 환경에서의 가시화 서버의 설계를 제안한다. 본 논문에서 제안하는 가시화 서버는 가시화 관리자, 네트워크 관리자, 데이터 관리자로 구분되며 이들 관리자를 통해 복수의 사용자에 대한 가시화 서비스 제공, 거대 데이터의 실시간 동적 데이터 분할 및 할당 및 실시간 가시화를 지원한다.