• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.141-148
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• 2017

The current advancement of mobile devices enables users to collect a sequence of user positions by use of the positioning technology and thus the related research regarding positioning or location information are quite arising. An individual mobility model based on positioning data and time data are already established while group mobility model is not done yet. In this research, group mobility model, an extension of individual mobility model, and the process of establishment of group mobility model will be studied. Based on the previous research of group mobility model from two individual mobility model, a group mobility model with more than two individual model has been established and the transition pattern of the model is represented by Markov chain. In consideration of real application, the computing time to establish group mobility mode from huge positioning data has been drastically improved by use of GPGPU comparing to the use of traditional multicore systems.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.635-642
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• 2003

The radical development of recent network technology provides the basic foundation which can establish a high speed and cheap cluster system. It is a general trend that conventional cluster systems are built as the system over a fixed level based on stabilized and high speed local networks. A multi-distributed web cluster group is a web cluster model which can obtain high performance, high efficiency and high availability through mutual cooperative works between effective job division and system nodes through parallel performance of a given work and shared memory of SC-Server with low price and low speed system nodes on networks. For this, multi-distributed web cluster group builds a sub-cluster group bound with single imaginary networks of multiple system nodes and uses the web distributed shared memory of system nodes for the effective data transmission within sub-cluster groups. Since the presented model uses a load balancing and parallel computing method of large-scale work required from users, it can maximize the processing efficiency.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.41-47
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• 2014

In this paper, we implemented a CUDA Fortran parallel program to run the CFD_NIMR model on GP-GPU's, which simulates air diffusion on urban terrains. A GP-GPU is graphic processing unit in the form of a PCI card, and a general calculation accelerator to perform a large amount of high speed calculations with low cost and electric power. The GP-GPU gives performance enhancement of speed by 15 times to compare the Nvidia Tesla C1060 GPU with Intel XEON 2.0 GHz CPU. In addition, the program on a GP-GPU shows efficient performance compared to an MPI parallel program on multiple CPU's. It is expected that a proposed programming method on the GP-GPU parallel program can be used for numerical models with a similar structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6B
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• pp.379-385
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• 2012

Recent development of computing power and theoretical advances in computational fluid dynamics have made possible numerical simulations of water waves with full Navier-Stokes equations. In this study, an internal wave maker using the mass source function approach was combined with the level set finite element method for generation of waves. The model is first applied to the two-dimensional linear wave generation and propagation. Then, it is applied to the three-dimensional simulation of the same problem. To effectively utilize computational resources and enhance the speed of execution, parallel algorithms are developed and applied for the three-dimensional problem. The results of numerical simulations are compared with theoretical values and good agreements are observed.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.183-194
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• 2015

This paper proposes a novel method for visualizing the thickness and clearance of 3D objects in a polyhedral representation. The proposed method uses the distance field of the objects in the visualization. A parallel algorithm is developed for constructing the distance field of polyhedral objects using the GPU. The distance between a voxel and the surface polygons of the model is computed many times in the distance field construction. Similar sets of polygons are usually selected as close polygons for close voxels. By using this spatial coherence, a parallel algorithm is designed to compute the distances between a cluster of close voxels and the polygons selected by the culling operation so that the fast shared memory mechanism of the GPU can be fully utilized. The thickness/clearance of the objects is visualized by distributing points on the visible surfaces of the objects and painting them with a unique color corresponding to the thickness/clearance values at those points. A modified ray casting method is developed for computing the thickness/clearance using the distance field of the objects. A system based on these algorithms can compute the distance field of complex objects within a few minutes for most cases. After the distance field construction, thickness/clearance visualization at a near interactive rate is achieved.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.164-173
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• 2017

Alternative cropping systems would be one of climate change adaptation options. Suitable areas for a crop could be identified using a climate suitability model. The EcoCrop model has been used to assess climate suitability of crops using monthly climate surfaces, e.g., the digital climate map at high spatial resolution. Still, a high-performance computing approach would be needed for assessment of climate suitability to take into account a complex terrain in Korea, which requires considerably large climate data sets. The objectives of this study were to implement a script for R, which is an open source statistics analysis platform, in order to use the EcoCrop model under a parallel computing environment and to assess climate suitability of maize using digital climate maps at high spatial resolution, e.g., 1 km. The total running time reduced as the number of CPU (Central Processing Unit) core increased although the speedup with increasing number of CPU cores was not linear. For example, the wall clock time for assessing climate suitability index at 1 km spatial resolution reduced by 90% with 16 CPU cores. However, it took about 1.5 time to compute climate suitability index compared with a theoretical time for the given number of CPU. Implementation of climate suitability assessment system based on the MPI (Message Passing Interface) would allow support for the digital climate map at ultra-high spatial resolution, e.g., 30m, which would help site-specific design of cropping system for climate change adaptation.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.351-359
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• 2009

Recently with increase of data in the Internet, platform technologies that can process huge data effectively such as Google platform and Hadoop are regarded as worthy of notice. In this kind of platform, there exist network I/O overheads to send task outputs due to the MapReduce operation which is a programming model to support parallel computation in the large cluster system. In this paper, we suggest applying of TIPC (Transparent Inter-Process Communication) protocol for reducing network I/O overheads and increasing network performance in the distributed computing environments. TIPC has a lightweight protocol stack and it spends relatively less CPU time than TCP because of its simple connection establishment and logical addressing. In this paper, we analyze main features of the Hadoop-based distributed computing system, and we build an experimental model which can be used for experiments to compare the performance of various protocols. In the experimental result, TIPC has a higher bandwidth and lower CPU overheads than other protocols.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Journal of Astronomy and Space Sciences
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• v.38 no.1
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• pp.55-63
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• 2021

The universe is well known to be consists of dark energy, dark matter and the standard model (SM) particles. The dark matter dominates the density of matter in the universe. The dark matter is thought to be linked with dark photon which are hypothetical hidden sector particles similar to photons in electromagnetism but potentially proposed as force carriers. Due to the extremely small cross-section of dark matter, a large amount of data is needed to be processed. Therefore, we need to optimize the central processing unit (CPU) time. In this work, using MadGraph5 as a simulation tool kit, we examined the CPU time, and cross-section of dark matter at the electron-positron collider considering three parameters including the center of mass energy, dark photon mass, and coupling constant. The signal process pertained to a dark photon, which couples only to heavy leptons. We only dealt with the case of dark photon decaying into two muons. We used the simplified model which covers dark matter particles and dark photon particles as well as the SM particles. To compare the CPU time of simulation, one or more cores of the KISTI-5 supercomputer of Nurion Knights Landing and Skylake and a local Linux machine were used. Our results can help optimize high-energy physics software through high-performance computing and enable the users to incorporate parallel processing.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.812-819
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• 2015

This paper presents a new approach for simulating the internal faults of synchronous machines using distributed computing and Large Change Sensitivity (LCS) analysis. LCS analysis caters for a parallel solution of 3-phase model of a faulted machine within the symmetrical component-based model of interconnected network. The proposed method considers dynamic behavior of the faulty machine and connected system and tries to accurately solve the synchronous machine’s internal fault conditions in the system. The proposed method is implemented in stand-alone FORTRAN-based phasor software and the results have been compared with available recordings from real networks and precisely simulated faults by use of the ATP/EMTP as a time domain software package. An encouraging correlation between the simulation results using proposed method, ATP simulation and measurements was observed and reported. The simplified approach also enables engineers to quickly investigate their particular cases with a reasonable precision.