• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2012년도 춘계학술발표대회논문집
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• pp.343-344
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• 2012

With the increase of sensitive data and their secure transmission and storage, the use of encryption techniques has become widespread. The performance of encoding majorly depends on the computational time, so a system with less computational time suits more appropriate as compared to its contrary part. Double Random Phase Encoding (DRPE) is an algorithm with many sub functions which consumes more time when executed serially; the computation time can be significantly reduced by implementing important functions in a parallel fashion on Graphics Processing Unit (GPU). Computing convolution using Fast Fourier transform in DRPE is the most important part of the algorithm and it is shown in the paper that by performing this portion in GPU reduced the execution time of the process by substantial amount and can be compared with MATALB for performance analysis. NVIDIA graphic card GeForce 310 is used with CUDA C as a programming language.

• Journal of information and communication convergence engineering
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• 제12권2호
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• pp.128-134
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• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• 한국항행학회논문지
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• 제15권4호
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• pp.616-625
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• 2011

본 연구에서는 지형을 Rendering 기법의 대표적인 방법인 Geometry Clipmaps와 ROAM 2.0을 분석하여 Rendering 연산에 소요되는 연산을 CPU가 아닌 GPU에 중점을 두어 보다 빠르고 넓은 가시화 영역을 보장하는 확장된 Geometry Clipmaps 알고리즘을 제안한다. 확장된 알고리즘은 LOD(Level of Detail)을 통한 각 레벨의 Mesh 구성 방법, 레벨간의 연결망 Mesh 구성 방법, VFC(View Frustum Culling)을 사용하여 Rendering을 최적화 할 수 있는 Mesh Block화 방안 그리고 최대 1m 해상도를 갖는 고해상도 영상 Mapping 방안 등을 포함하고 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.761-764
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• 2013

n개의 원소로 이루어진 집합 S에서 r개의 원소를 선택하여 만들 수 있는 모든 조합을 평가하여 최적의 조합을 찾아내는 것은 많은 공학적 문제를 일반화하여 풀 수 있는 방법이다. 조합 전수조사 알고리즘은 경우의 수가 매우 크거나 각 조합을 평가하는데 많은 시간이 소요될 경우 알고리즘의 수행 속도가 급격히 저하되는 문제가 있다. 본 논문은 CUDA를 이용하여 각각의 조합을 GPU상의 스레드에서 병렬적으로 평가하는 기법을 제안한다. 실험 결과는 GPU상에서 동작하는 병렬적인 알고리즘이 CPU상에서 동작하는 순차적인 알고리즘에 비해 최대 약 900배의 성능 향상이 있음을 보인다.

• 대한기계학회논문집A
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• 제38권1호
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• pp.17-23
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• 2014

그래픽 처리장치(GPU)는 병렬적인 정보를 포함하는 문제를 해결하는데 이상적이다. 본 연구에서는 GPU 는 입자동역학과 함께 다물체 동역학 시뮬레이션을 효율적으로 수행하기 위해 사용되었다. 수치계산을 위해서 HHT 암시적 적분 알고리즘이 사용되었다. 입자들 사이의 접촉을 판별하기 위해서 공간 분할 알고리즘과 입자 거동 해석법으로 이산 요소법(DEM)이 사용되었다. 개발된 다물체 동역학 프로그램은 해는 ADAMS 프로그램의 결과와 비교 검증하였다. CPU 기반의 순차해석 프로그램과 GPU 기반 병렬 프로그램은 입자의 수에 따른 수치계산 효율성을 알아보기 위해 서로 비교되었으며, 입자의 수가 많아질수록 계산시간은 단축되었다. 본 예제에서 입자의 수가 1,300 개일 때, 순차 해석 프로그램보다 병렬 프로그램이 약 5 배 가량 빠른 계산 속도를 보였다.

• 대한기계학회논문집B
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• 제40권9호
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• pp.597-604
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• 2016

본 연구에서는 GPU를 이용한 비압축성 유동장의 병렬연산을 위하여, P2P1 유한요소를 이용한 분리 알고리즘 내의 행렬 해법인 이중공액구배법(Bi-Conjugate Gradient)의 CUDA 기반 알고리즘을 개발하였다. 개발된 알고리즘을 이용해 비대칭 협착관 유동을 해석하고, 단일 CPU와의 계산시간을 비교하여 GPU 병렬 연산의 성능 향상을 측정하였다. 또한, 비대칭 협착관 유동 문제와 다른 행렬 패턴을 가지는 유체구조 상호작용 문제에 대하여 이중공액구배법 내의 희소 행렬과 벡터의 곱에 대한 GPU의 병렬성능을 확인하였다. 개발된 코드는 희소 행렬의 1개의 행과 벡터의 내적을 병렬 연산하는 커널(Kernel)로 구성되며, 최적화는 병렬 감소 연산(Parallel Reduction), 메모리 코얼레싱(Coalescing) 효과를 이용하여 구현하였다. 또한, 커널 생성 시 워프(Warp)의 크기에 따른 성능 차이를 확인하였다. 표준예제들에 대한 GPU 병렬연산속도는 CPU 대비 약 7배 이상 향상됨을 확인하였다.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.411-417
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• 2016

Since the development of Graphic Processing Unit (GPU) in 1999, the development speed of GPUs has become much faster than that of CPUs and currently, the computational power of GPUs exceeds CPUs dozens and hundreds times in terms of decimal calculations and costs much less. Owing to recent technological development of hardwares, general-purpose computing and utilization using GPUs are on the rise. Thus, in this paper, we have identified the elements to be considered for the Smart Grid Security. Focusing on a Performance Improvement of the Basic Algorithm for the Stateful Inspection to Detect DDoS Attacks using CUDA. In the program, we compared the search speeds of GPU against CPU while they search for the suffix trees. For the computation, the system constraints and specifications were made identical during the experiment. We were able to understand from the results of the experiment that the problem-solving capability improves when GPU is used. The other finding was that performance of the system had been enhanced when shared memory was used explicitly instead of a global memory as the volume of data became larger.

• Tribology and Lubricants
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• 제28권4호
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• pp.160-166
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• 2012

This paper presents a Reynolds equation solver for hydrostatic gas bearings, implemented to run on graphics processing units (GPUs). The original analysis code for the central processing unit (CPU) was modified for the GPU by using the compute unified device architecture (CUDA). The red-black Gauss-Seidel (RBGS) algorithm was employed instead of the original Gauss-Seidel algorithm for the iterative pressure solver, because the latter has data dependency between neighboring nodes. The implemented GPU program was tested on the nVidia GTX580 system and compared to the original CPU program on the AMD Llano system. In the iterative pressure calculation, the implemented GPU program showed 20-100 times faster performance than the original CPU codes. Comparison of the wall-clock times including all of pre/post processing codes showed that the GPU codes still delivered 4-12 times faster performance than the CPU code for our target problem.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1769-1780
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• 2022

A new method of dose calculation algorithm, called GPU-accelerated Monte Carlo and collapsed cone Convolution (GMCC) was developed to improve the calculation speed of BNCT treatment planning system. The GPU-accelerated Monte Carlo routine in GMCC is used to simulate the neutron transport over whole energy range and the Collapsed Cone Convolution method is to calculate the gamma dose. Other dose components due to alpha particles and protons, are calculated using the calculated neutron flux and reaction data. The mathematical principle and the algorithm architecture are introduced. The accuracy and performance of the GMCC were verified by comparing with the FLUKA results. A water phantom and a head CT voxel model were simulated. The neutron flux and the absorbed dose obtained by the GMCC were consistent well with the FLUKA results. In the case of head CT voxel model, the mean absolute percentage error for the neutron flux and the absorbed dose were 3.98% and 3.91%, respectively. The calculation speed of the absorbed dose by the GMCC was 56 times faster than the FLUKA code. It was verified that the GMCC could be a good candidate tool instead of the Monte Carlo method in the BNCT dose calculations.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.1966-1973
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• 2023

Virtual reality technology has been widely used in the field of nuclear and radiation safety, dose rate computing in virtual environment is essential for optimizing radiation protection and planning the work in radioactive-controlled area. Because the CPU-based gamma dose rate computing takes up a large amount of time and computing power for voxelization of volumetric radioactive source, it is inefficient and limited in its applied scope. This study is to develop an efficient gamma dose rate computing code and apply into fast virtual simulation. To improve the computing efficiency of the point kernel algorithm in the reference (Li et al., 2020), we design a GPU-based computing framework for taking full advantage of computing power of virtual engine, propose a novel voxelization algorithm of volumetric radioactive source. According to the framework, we develop the GPPK(GPU-based point kernel gamma dose rate computing) code using GPU programming, to realize the fast dose rate computing in virtual world. The test results show that the GPPK code is play and plug for different scenarios of virtual simulation, has a better performance than CPU-based gamma dose rate computing code, especially on the voxelization of three-dimensional (3D) model. The accuracy of dose rates from the proposed method is in the acceptable range.