• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.189-198
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• 2024

Research combining deep learning-based models and physical simulations is making important advances in the medical field. This extracts the necessary information from medical image data and enables fast and accurate prediction of deformation of the skeleton and soft tissue based on physical laws. This study proposes a system that integrates Neural Radiance Fields (NeRF), Position-Based Dynamics (PBD), and Parallel Resampling to generate 3D volume data, and deform and visualize them in real-time. NeRF uses 2D images and camera coordinates to produce high-resolution 3D volume data, while PBD enables real-time deformation and interaction through physics-based simulation. Parallel Resampling improves rendering efficiency by dividing the volume into tetrahedral meshes and utilizing GPU parallel processing. This system renders the deformed volume data using ray casting, leveraging GPU parallel processing for fast real-time visualization. Experimental results show that this system can generate and deform 3D data without expensive equipment, demonstrating potential applications in engineering, education, and medicine.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.991-998
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• 2022

The purpose of this study is to develop GPU (Graphics Processing Unit) acceleration technique for 2-dimensional model and to assess the effectiveness for high resolution flood simulation in wide area In this study, GPU acceleration technique was implemented in the G2D (Grid based 2-Dimensional land surface flood model) model, using implicit scheme and uniform square grid, by using CUDA. The technique was applied to flood simulation in Jinju-si. The spatial resolution of the simulation domain is 10 m × 10 m, and the number of cells to calculate is 5,090,611. Flood period by typhoon Mitag, December 2019, was simulated. Rainfall radar data was applied to source term and measured discharge of Namgang-Dam (Ilryu-moon) and measured stream flow of Jinju-si (Oksan-gyo) were applied to boundary conditions. From this study, 2-dimensional flood model could be implemented to reproduce the measured water level in Nam-gang (Riv.). The results of GPU acceleration technique showed more faster flood simulation than the serial and parallel simulation using CPU (Central Processing Unit). This study can contribute to the study of developing GPU acceleration technique for 2-dimensional flood model using implicit scheme and simulating land surface flood in wide area.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.633-640
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• 2022

As service robots are applied to various fields, interest in an image processing processor that can perform an image processing algorithm quickly and accurately suitable for each task is increasing. This paper introduces an image processing processor design method applicable to robots. The proposed processor consists of an AGX board, FPGA board, LiDAR-Vision board, and Backplane board. It enables the operation of CPU, GPU, and FPGA. The proposed method is verified through simulation experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.119-128
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• 2019

Due to the nature of the 3D graphics processor system, many mathematical calculations are required and parallel processing research using GPU (Graphics Processing Unit) is being performed for high-speed processing. In this paper, we propose a 3D graphics processor using MAMS, a parallel processor that does not use cache memory, to solve the GPU problem of increasing bandwidth caused by cache memory miss and the problem that 3D shader processing speed is not constant. The 3D graphics processor using MAMS proposed in this paper designed Vertex shader, Pixel shader, Tiling and Rasterizing structure using DirectX command analysis, the FPGA(Xilinx Virtex6@100MHz) board for MAMS was constructed and designed using Verilog. We compared the processing time of the developed FPGA (100Mhz) and nVidia GeForce GTX 660 (980Mhz), the processing time using GTX 660 was not constant and suing MAMS was constant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2761-2781
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• 2018

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.887-894
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• 2019

We proposed a GPU (Grapic Processing Unit) accelerated kinematic wave model for rainfall runoff simulation and tested the accuracy and speed up performance of the proposed model. The governing equations are the kinematic wave equation for surface flow and the Green-Ampt model for infiltration. The kinematic wave equations were discretized using a finite volume method and CUDA fortran was used to implement the rainfall runoff model. Several numerical tests were conducted. The computed results of the GPU accelerated kinematic wave model were compared with several measured and other numerical results and reasonable agreements were observed from the comparisons. The speed up performance of the GPU accelerated model increased as the number of grids increased, achieving a maximum speed up of approximately 450 times compared to a CPU (Central Processing Unit) version, at least for the tested computing resources.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.423-431
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• 2021

SPECK and SIMON are lightweight block ciphers developed by NSA(National Security Agency), and SIMECK is a new lightweight block cipher that combines the advantages of SPECK and SIMON. In this paper, a large-capacity encryption using SPECK, SIMON, and SIMECK is implemented using a GPU with efficient parallel processing. CUDA library provided by NVIDIA was used, and performance was maximized by using CUDA assembly language PTX to eliminate unnecessary operations. When comparing the results of the simple CPU implementation and the implementation using the GPU, it was possible to perform large-scale encryption at a faster speed. In addition, when comparing the implementation using the C language and the implementation using the PTX when implementing the GPU, it was confirmed that the performance increased further when using the PTX.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.29-42
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• 2016

Remote sensing allows acquisition of information across a large area without contacting objects, and has thus been rapidly developed by application to different areas. Thus, with the development of remote sensing, satellites are able to rapidly advance in terms of their image resolution. As a result, satellites that use remote sensing have been applied to conduct research across many areas of the world. However, while research on remote sensing is being implemented across various areas, research on data processing is presently insufficient; that is, as satellite resources are further developed, data processing continues to lag behind. Accordingly, this paper discusses plans to maximize the performance of satellite image processing by utilizing the CUDA(Compute Unified Device Architecture) Library of NVIDIA, a parallel processing technique. The discussion in this paper proceeds as follows. First, standard KOMPSAT(Korea Multi-Purpose Satellite) images of various sizes are subdivided into five types. NDVI(Normalized Difference Vegetation Index) is implemented to the subdivided images. Next, ArcMap and the two techniques, each based on CPU or GPU, are used to implement NDVI. The histograms of each image are then compared after each implementation to analyze the different processing speeds when using CPU and GPU. The results indicate that both the CPU version and GPU version images are equal with the ArcMap images, and after the histogram comparison, the NDVI code was correctly implemented. In terms of the processing speed, GPU showed 5 times faster results than CPU. Accordingly, this research shows that a parallel processing technique using CUDA Library can enhance the data processing speed of satellites images, and that this data processing benefits from multiple advanced remote sensing techniques as compared to a simple pixel computation like NDVI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4467-4486
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• 2016

Discrete cosine transform (DCT) based JPEG standard significantly improves the coding efficiency of image compression, but it is unacceptable event in serious blocking artifacts at low bit rate and low efficiency of high-definition image. In the light of all phase digital filtering theory, this paper proposes a novel transform based on discrete sine transform (DST), which is called all phase discrete sine biorthogonal transform (APDSBT). Applying APDSBT to JPEG scheme, the blocking artifacts are reduced significantly. The reconstructed image of APDSBT-JPEG is better than that of DCT-JPEG in terms of objective quality and subjective effect. For improving the efficiency of JPEG coding, the structure of JPEG is analyzed. We analyze key factors in design and evaluation of JPEG compression on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. Experimental results show that the maximum speedup ratio of parallel algorithm of APDSBT-JPEG can reach more than 100 times with a very low version GPU. Some new parallel strategies are illustrated in this paper for improving the performance of parallel algorithm. With the optimal strategy, the efficiency can be improved over 10%.