• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.9-15
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• 2017

With the development of semiconductor integration technology, central processing units and storage devices have been miniaturized and performance has been rapidly developed, interconnection network technology is becoming a more important factor in terms of the performance of high performance computing system. In this paper, we analyze the trend of interconnection network technology used in high performance computing. Interconnect technology, which is the most widely used in the Supercomputer Top 500(2017. 06.), is an Infiniband. Recently, Ethernet is the second highest share after InfiniBand due to the emergence of 40/100Gbps Gigabit Ethernet technology. Gigabit Ethernet, where latency performance is lower than InfiniBand, is preferred in cost-effective medium-sized data centers. In addition, top-end HPC systems that demand high performance are devoting themselves from Ethernet and InfiniBand technologies and are attempting to maximize system performance by introducing their own interconnect networks. In the future, high-performance interconnects are expected to utilize silicon-based optical communication technology to exchange data with light.

• Annual Conference of KIPS
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• 2017.04a
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• pp.190-191
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• 2017

정보통신기술(ICT)의 발전으로 원격지에서 고속으로 HPC(High Performance Computing) 서비스를 이용할 수 있게 되었지만, HPC 서비스 환경을 대상으로 하는 사이버 공격도 끊이지 않고 발생하고 있다. 본 논문에서는 슈퍼컴퓨터 4호기 서비스 환경에서 탐지/차단된 사이버 공격 증가 추이를 살펴보고, 2016년 사이버 공격 탐지 데이터와 슈퍼컴퓨팅서비스 네트워크 내부로 유입된 트래픽 데이터를 분석하여 급격히 증가한 공격지 IP 주소의 분포 및 특징을 확인한다.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.4
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• pp.1-8
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• 2022

High-density packaging technologies, including Through-Si-Via (TSV) technologies, are considered important in many fields such as IoT (internet of things), 6G/5G (generation) communication, and high-performance computing (HPC). Achieving high integration in two dimensional packaging has confronted with physical limitations, and hence various studies have been performed for the three-dimensional (3D) packaging technologies. In this review, we described about the causes and effects of scallop formation in TSV, the scallop-free etching technique for creating smooth sidewalls, Cu pillar and Cu-SiO₂ hybrid bonding in TSV. These technologies are expected to have effects on the formation of high-quality TSVs and the development of 3D packaging technologies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.554-557
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• 2016

Recently, supercomputer has been increasingly adopted as a computing environment for scientific simulation as well as education, healthcare and national defence. Especially, supercomputing system with heterogeneous computing resources is gaining resurgence of interest as a next-generation problem solving environment, allowing theoretical and/or experimental research in various fields to be free of time and spatial limits. However, traditional supercomputing services have only been handled through a simple form of command-line based console, which leads to the critical limit of accessibility and usability of heterogeneous computing resources. To address this problem, in this paper, we provide the design and implementation of web-based HPC (High Performance Computing) job management framework for computational scientific simulation. The proposed framework has highly extensible design principles, providing the abstraction interfaces of job scheduler (as well as bundle scheduler plug-ins for LoadLeveler, Sun Grid Engine, OpenPBS scheduler) in order to easily incorporate the broad spectrum of heterogeneous computing resources such as cluster, computing cloud and grid. We also present the detailed specification of HTTP standard based RESTful endpoints, which manage simulation job's life-cycles such as job creation, submission, control and status monitoring, etc., enabling various 3rd-party applications to be newly created on top of the proposed framework.

• Journal of Platform Technology
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• v.6 no.4
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• pp.49-58
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• 2018

Complex disasters make greater damage and higher losses unexpected than the past. To prevent these disasters, it needs to prepare a plan for handling unexpected results. Especially an accident at a facility like an atomic power plant makes a big problem cause of climate change. A simulation needs to do preliminary researches based on diverse situations. In this research we define the basic component techniques to design and implement the disaster management system. Basically a hierarchical system design method is to build on the resources provided from high performance computing(HPC) and large-scale storage systems. To develop the system, it is considered middleware as well as application studies, data studies and decision making services in convergence areas.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.21-31
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• 2018

In order to utilize HPC in Computational science, It is necessary to learn the knowledge and skills of computer science such as programming, algorithms and data structure. In this paper, we investigate IT education status in Computational science and propose policy directions to improve the HPC education through user survey. To do this, we surveyed the current state of IT subjects among major subjects in physics, chemistry, life sciences, and earth science in domestic universities and surveyed the users' Recognition of HPC education. As a result, the ratio of IT subjects in Computational science was very lower than the ratio of major domain subjects. Despite the high educational needs of universities, the educational level of universities was the lowest. Most users have learned the necessary knowledge and skills through self-study. We recognized the role of the university is the most urgent and important, and the role of professional institutions and online education is also important.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.76-79
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• 2008

In this work wood crib burning behaviors have been simulated by using the FDS(Fire Dynamic Simulator) program. Wood cribs are regularly stacked arrays of wood sticks, and available for the performance rating of fire-extinguishers. On the basis of an angle iron supporter 26 layers of wood sticks have been stacked up. Each layer consists of 5 or 6 wood sticks which are placed in parallel, with a constant distance, and in alternating rows. They are laid between the horizontally adjacent sticks at the before last layer. The wood crib is ignited instantaneously by an amount of burning gasoline below. A comprehensive simulation of such a practical sophisticated combustion is still too difficult to realize with any currently available computer, although the performance of modern processors is getting better everyday. We could carry it out here through parallel computing on the HPC(High Performance Computing) cluster as the feasible alternative. At last the validation has been executed by means of temperature distribution data measured by the thermal video camera.

• The KIPS Transactions:PartA
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• v.15A no.5
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• pp.259-268
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• 2008

Multi-Core processors have become main-stream microprocessors in recent years. Servers based on these multi-core processors are widely adopted in High Performance Computing (HPC) and commercial business applications as well. These servers provide increased level of parallelism, thus can potentially boost the performance for applications. However, the shared resources among multiple cores on the same chip can become hot spots and act as performance bottlenecks. Therefore it is essential to optimize the use of shared resources for high performance and scalability for the multi-core servers. In this paper, we conduct experimental studies to analyze the positive and negative effects of the resource sharing on the performance of HPC applications. Through the analyses we also characterize the performance of multi-core servers.