• Journal of The Korean Association of Information Education
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• v.22 no.5
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• pp.545-556
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• 2018

The purpose of this study is to suggest the knowledge elements for HPC education in computational science. For this purpose, the survey for HPC experts was conducted to verify the content validity and reliability, and the 20 candidate knowledge elements was extracted. And the second survey for HPC users was conducted to apply the t test, Borich requirement, and The Locus for Focus model. And 10 knowledge elements for HPC education were derived. As a result, the first group was 'Parallelism Fundamentals', 'Parallelism', 'Parallel communication and coordination', 'Parallel Decomposition', 'Parallel Algorithms, Analysis, and Programming' and 'Introduction to Modeling and Simulation', 'Fundamental Programming Concepts', 'Fundamental Data Structures', 'Memory Management', 'Algorithms and Design' were second group for HPC education.

• 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.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.1021-1026
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• 2018

Computational science is at an early stage and is not yet fully active, and the high-performance computing required in the field of computational science is at present a special subject of parallel and distributed computing in computer science. Additionally, there are too few education courses which teach high-performance computing from basic to advanced levels. In this study, we derive the knowledge units needed to learn high-performance computing, an important research tool in computational science. Using ACM the Computer Science Curricula 2013 (CS2013), we examine the validity and reliability of 89 knowledge units and eleven knowledge units with high validity and reliability, after which nine core knowledge units and two optional knowledge units are proposed. The eleven proposed knowledge units are expected to contribute to the development of the high-performance computing curriculum necessary to teach computational science.

• Proceedings of The KACE
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• 2017.08a
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• pp.63-64
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• 2017

정보 기술의 끊임없는 발전에 따라 광범위한 분야에서 방대한 양의 데이터가 발생하게 되면서 이를 처리하기 위한 빅 데이터에 대한 연구 및 교육이 활발히 진행되고 있다. 이를 위하여 데이터 분석 및 처리를 위한 고성능의 서버 및 분산 처리를 위한 다수의 컴퓨터가 필요하며 이는, 개인 혹은 저사양의 수업 환경에서 빅 데이터를 학습하는 데에 어려움을 겪게 한다. 때문에 가상 환경에서 원활한 빅 데이터 학습을 위한 클라우드 기반의 시스템이 필요하다. 이에 본 논문에서는, 빅 데이터 처리 기술의 하나인 Spark를 이용한 빅 데이터 플랫폼 구축에 대하여 기술한다.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.3
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• pp.60-71
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• 2017

Smart devices such as smart phones, smart TVs, and smart pads have become essential devices in recent years. As the popularity and demand grows, the performance of smart devices is also getting better and users are dealing with a lot of things such as education and business using smart devices instead of desktop. However, smart devices that still have poor performance compared to desktop, even with improved performance, have difficulty running high performance applications due to limited resources. In this paper, we propose a load balancing algorithm applying the characteristics of smart devices to overcome the resource limitations of devices. in order to verify the algorithm, we implemented the algorithm after adding the distributed processing system service in Android platform. After constructing the cluster on the smart device, various experiments were conducted. Through the analysis of the test results, it is confirmed that the proposed algorithm efficiently improves the overall distributed processing performance by effectively aggregating different amounts of computing resources in heterogeneous smart devices.