• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.5
• /
• pp.37-43
• /
• 2003

Parallel computing method is widely used in the computational fluid dynamics for efficient numerical analysis. Nowadays, low cost Linux cluster computers substitute for traditional supercomputers with parallel computing shcemes. The performance of nemerical solvers on an Linux cluster computer is highly dependent not on the performance of processors but on the performance of network devices in the cluster system. In this paper, we investigated the effects of the network devices such as Myrinet2000, gigabit ethernet, and fast ethernet on the performance of the cluster system by using some benchmark programs such as Netpipe, LINPACK, NAS NPB, and MPINS2D Navier-Stokes solvers. Finally, upon this investigation, we will suggest the method for building high performance low cost Linux cluster system in the computational fluid dynamics analysis.

• Journal of Internet Computing and Services
• /
• v.7 no.6
• /
• pp.87-103
• /
• 2006

Advances in computer and network technology provide new computing environment that were only possible with supercomputers before. In order to provide the environment, a distributed runtime system has to be provided, but most of the conventional distributed runtime systems lack in providing dynamic and flexible system reconfiguration depending on workload variance, due to a static architecture of fixed master node and slave working nodes. This paper proposes and implements a new model for distributed job allocation and management which is a distributed runtime system is P2P environment for flexible and dynamic system reconfiguration. The implemented systems enables job program transfer and management, remote compile and execution among cooperative developers based on P2P standard protocol Jxta platform. Since it makes dynamic and flexible system reconfiguration possible, the proposed method has some advantages in that it can collect and utilize idle computing resources immediately at a needed time for distributed job processing. Moreover, the implemented system's effectiveness and performance increase are shown by applying and processing the crawler jobs, in a distributed way, for collecting a large amount of data needed for internet search.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.466-469
• /
• 2011

Recently, a new theoretical and methodological approach for computational science is becoming more and more popular for analyzing and solving scientific problems in various scientific disciplines such as Computational fluid dynamics, Chemistry, Physics, Structural Dynamics, Computational Design and applied research. Computational science is a field of study concerned with constructing mathematical models and quantitative analysis techniques and using large computing resources to solve the problems which are difficult to approach in a physical experimentally. In this paper, we present R&D of EDISON open integration platform that allows anyone like professors, researchers, industrial workers, students etc to upload their advanced research result such as simulation SW to use and share based on the cyber infrastructure of supercomputer and network. EDISON platform, which consists of 3 tiers (EDISON application framework, EDISON middleware, and EDISON infra resources) provides Web portal for education and research in 5 areas (CFD, Chemistry, Physics, Structural Dynamics, Computational Design) and user service.

• Journal of KIISE:Computing Practices and Letters
• /
• v.8 no.1
• /
• pp.107-115
• /
• 2002

We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.1
• /
• pp.33-40
• /
• 2023

With the development of supercomputing technology and hardware technology, numerical computation methods are also being advanced. Accordingly, improved weather prediction becomes possible. In this paper, we propose to apply the LAPACK(Linear Algebra PACKage) BLAS(Basic Linear Algebra Subprograms) library to the linear algebraic numerical computation part within the source code to improve the performance of the cumulative parametric code, Unicon(A Unified Convection Scheme), which is included in SCAM(Single-Columns Atmospheric Model, simplified version of CESM(Community Earth System Model)) and performs standby operations. In order to analyze this, an overall execution structure diagram of SCAM was presented and a test was conducted in the relevant execution environment. Compared to the existing source code, the SCOPY function achieved 0.4053% performance improvement, the DSCAL function 0.7812%, and the DDOT function 0.0469%, and all of them showed a 0.8537% performance improvement. This means that the LAPACK BLAS application method, a library for high-density linear algebra operations proposed in this paper, can improve performance without additional hardware intervention in the same CPU environment.

• Journal of the Korean earth science society
• /
• v.43 no.6
• /
• pp.691-711
• /
• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.

• Proceedings of the Korea Contents Association Conference
• /
• 2007.11a
• /
• pp.522-526
• /
• 2007

Since newly emerged disease, the Severe Acute Respiratory Syndrome (SARS), spread from Asia to North America and Europe rapidly in 2003, many researchers have tried to determine where the virus came from. In the phylogenetic point of view, SARS virus has been known to be one of the genus Coronavirus, but, the overall conservation of SARS virus sequence was not highly similar to that of known coronaviruses. The natural reservoirs of SARS-CoV are not clearly determined, yet. In the present study, the genomic sequences of SARS-CoV were analyzed by bioinformatics techniques such as multiple sequence alignment and phylogenetic analysis methods as well multivariate statistical analysis. All the calculating processes, including calculations of the relative synonymous codon usage (RSCU) and other genomic parameters using 30,305 coding sequences from the two genera, Coronavirus, and Lentivirus, and one family, Orthomyxoviridae, were performed on SMP cluster in KISTI, Supercomputing Center. As a result, SARS_CoV showed very similar RSCU patterns with feline coronavirus on the both axes of the correspondence analysis, and this result showed more agreeable results with serological results for SARS_CoV than that of phylogenetic result itself. In addition, SARS_CoV, human immunodeficiency virus, and influenza A virus commonly showed the very low RSCU differences among each synonymous codon group, and this low RSCU bias might provide some advantages for them to be transmitted from other species into human beings more successfully. Large-scale genomic analysis using bioinformatics techniques may be useful in genetic epidemiology field effectively.

• Journal of Korean Society for Geospatial Information Science
• /
• v.7 no.2 s.14
• /
• pp.123-132
• /
• 1999

With the rapid increases of the amount of data and computing, the parallelization of the computing algorithm becomes necessary more than ever. However the parallelization had been conducted mostly in a super-computer until the rod 1990s, it was not for the general users due to the high price, the complexity of usage, and etc. A new concept for the parallel processing has been emerged in the form of K-clustering form the late 1990s, it becomes an excellent alternative for the applications need high computer power with a relative low cost although the installation and the usage are still difficult to the general users. The mapping algorithms (cut, join, resizing, warping, conversion from raster to vector and vice versa, etc) in GIS are well suited for the parallelization due to the characteristics of the data structure. If those algorithms are manipulated using PC-clustering, the result will be satisfiable in terms of cost and performance since they are processed in real flu with a low cos4 In this paper the tools and the libraries for the parallel processing and PC-clustering we introduced and how those tools and libraries are applied to mapping algorithms in GIS are showed. Parallel programs are developed for the mapping algorithms and the result of the experiments shows that the performance in most algorithms increases almost linearly according to the number of node.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.3
• /
• pp.286-290
• /
• 2008

As the number of mobile device users increases, many researches on peer-to-peer(P2P) systems in mobile environments have been carried out. In this paper, we propose a couple of double-layered P2P file sharing systems to overcome the 'flooding' problem in previous mobile P2P systems. We classify peers into two groups, super peers and sub-peers to establish new routing tables. A super peer manages its sub-peers in the systems. The first proposed system partitions the service area into small cells, each of which is a square. Each super peer is located near the center of the square. The second system selects super peers which have the largest number of adjacent peers. As file transmission and file searches are managed mainly by super peers, unnecessary multi-broadcasting could be avoided. The experimental results show that the proposed systems outperform a typical file sharing system in terms of the amount of message traffic with about $1.2{\sim}1.6$ times improvement on the average.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.6
• /
• pp.489-498
• /
• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.