• Journal of Internet Computing and Services
• /
• v.18 no.3
• /
• pp.29-36
• /
• 2017

In these days, 3D dynamic simulation is closely related to many industries. In the past, physically-based 3D simulation was used mainly in the car crash or construction related fields, but it also plays an important role in movies or games today. Many mathematical computations are needed to represent the 3D object realistically, but it is difficult to process a large amount of calculations for simulation of application based on CPU in real-time. Recently, with the advanced graphic hardware and improved architecture, GPU can be utilized for the general purposes of computation function as well as graphic computation. Many approaches using GPU have been applied for various research fields. In this paper, we analyze the performance variation of two cloth simulation algorithms based on GPU according to the change of execution properties of GPU shaders in oder to optimize the performance of GPU-based cloth simulation. Cloth simulation is implemented by the spring centric algorithm and node centric algorithm with GPU parallel computing using compute shader of GLSL 4.3. We compare the performance of between these algorithms according to the change of the size and dimension of work group. The experiment is repeated to 10 times during 5,000 frames for each test and experimental results are provided by averaging of FPS. The experimental result shows that the node centric algorithm is executed in higher speed than the spring centric algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.79-80
• /
• 2023

동기식 분산 딥러닝 기법은 그래디언트 계산 작업을 다수의 워커가 나누어 병렬 처리함으로써 모델 학습 과정을 효율적으로 단축시킨다. 배치 사이즈는 이터레이션 단위로 처리하는 데이터 개수를 의미하며, 학습 속도 및 학습 모델의 품질에 영향을 미치는 중요한 요소이다. 멀티 GPU 환경에서 작동하는 분산 학습의 경우, 가용 GPU 메모리 용량이 커짐에 따라 선택 가능한 배치 사이즈의 상한이 증가한다. 하지만 배치 사이즈가 학습 속도 및 학습 모델 품질에 미치는 영향은 GPU 활용률, 총 에포크 수, 모델 파라미터 개수 등 다양한 변수에 영향을 받으므로 최적값을 찾기 쉽지 않다. 본 연구는 동기식 분산 딥러닝 환경에서 실험을 통해 최적의 배치 사이즈 선택에 영향을 미치는 주요 요인을 분석한다.

• Journal of the Korea Computer Graphics Society
• /
• v.23 no.2
• /
• pp.1-10
• /
• 2017

A variety of filters are applied to improve the quality of noise and low resolution medical images. This is necessary to reduce the radiation dose of the patient and to improve the utilization of the conventional spherical imaging equipment. In the conventional method, it is common to perform filtering using the CPU of the PC. However, it is difficult to produce results in real time by applying various calculations and filters to high-resolution human images using only the CPU performance of a PC used in a hospital. In this paper, we analyze the structure and performance of Intel integrated GPU in CPU and propose a method to perform image filtering using OpenCL parallel processing function. By applying complex filters with high computational complexity to medical images, high quality images can be generated in real time.

• Journal of Korea Game Society
• /
• v.6 no.4
• /
• pp.55-61
• /
• 2006

Graphics rendering pipeline (application, geometry, and rasterizer) is the core of real-time graphics which is the most important functionality for computer games. Usually this rendering process is completed by both the CPU and the GPU, and a bottleneck can be located either in the CPU or the GPU. This paper focuses on reducing the bottleneck between the CPU and the GPU. We are proposing a method for improving the performance of parallel processing for real-time graphics rendering by separating the CPU operations (usually performed using a thread) into two parts: pure CPU operations and operations related to the GPU, and let them operate in parallel. This allows for maximizing the parallelism in processing the communication between the CPU and the GPU. Some experiments lead us to confirm that our method proposed in this paper can allow for faster graphics rendering. In addition to our method of using a dedicated thread for GPU related operations, we are also proposing an algorithm for balancing the graphics pipeline using the idle time due to the bottleneck. We have implemented the two methods proposed in this paper in our networked 3D game engine and verified that our methods are effective in real systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2013.06a
• /
• pp.389-390
• /
• 2013

모바일에서의 증강현실(Augmented Reality :AR) 어플리케이션은 디바이스의 구조상 많은 제약사항이 있기 때문에 데스크탑 환경에 비교하여 접근성이 낮다. 이러한 문제점을 해결하기 위해 다양한 방법의 연구가 진행되고 있다. 본 논문에서는 모바일 기기의 처리량을 줄이기 위해 프로그래밍 가능한 GPU(Graphic Processing Unit)를 이용, 영상처리 알고리즘을 병렬로 처리하고 고속화하여 모바일 AR 어플리케이션의 접근성을 높이는 비마커(Markerless)기반 객체 인식 시스템을 구현한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.05a
• /
• pp.389-391
• /
• 2013

대용량 영상 데이터 처리를 위한 GPU 는 많은 코어들을 이용한 병렬 작업을 통해 결과를 도출한다. 단순 수치 연산에 특화된 이러한 GPU 의 계산 능력을 다른 분야로 확장시켜 적용하고자 하는 시도인 GPGPU 는 이전부터 꾸준히 시도되고 있다. 그러나 GPU 의 난해하고 생소한 프로그래밍으로, 작성이 쉽지 않고 현격한 성능 향상을 기대하기 어렵다. 이에, 이러한 GPGPU 프로그래밍의 어려움을 해결하고자 여러 프로그래밍 모델들이 등장하였다. 본 논문에서는 GPGPU 프로그래밍을 위한 대표적인 모델인 CUDA, OpenCL, C++ AMP, 그리고 OpenACC 에 대해 살펴본다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.109-109
• /
• 2019

본 연구에서는 강우-유출 과정 모의를 위한 GPU 기반 확산파 모형을 개발하였다. 확산파 방정식을 풀기위한 수치기법으로는 유한체적법을 이용하였으며, van Leer TVD limiter를 적용한 MUSCL 기법을 이용하여 각 셀의 인터페이스의 물리적 성질을 재구성하여 구하였다. 또한, 침투를 고려하기 위하여 Horton 침투 모형을 이용하였다. 개발된 모형을 이용하여 1D single overland plane과 2D V-shaped overland에서 강우-유출 과정을 모의실험을 하였으며, 각각 해석해와 dynamic wave model을 이용하여 계산된 수치 결과와 비교하여 본 모형의 정확성을 검증하였다. 또한, 1D와 2D의 기복이 심한 지형에 적용하여 강우-유출과정이 본 모형을 통하여 물리적으로 타당한 해석이 가능함을 검증하였다. 마지막으로 복잡한 실제 지형에 적용하였으며, 측정값과의 비교를 통하여 실제 유역에서의 확산파 모형의 적정성을 검증하였다. 또한, 본 연구에서는 NVIDIA사의 GPU인 Geforce GTX 1050과 GPU의 병렬 연산 처리 능력을 활용할 수 있는 NVIDIA사의 CUDA-Fortran을 이용하여 GPU 기반 확산파 모형을 개발하였다. PC windows에서 CPU(Intel i7, 4.70 GHz) 기반 모형 대비 GPU 기반 모형의 계산속도 성능을 비교한 결과, 격자 간격이 증가할수록 CPU 기반 모형 대비 GPU 기반 모형의 연산 효율이 증가하였으며, 격자 간격이 $3200{\times}3200$일 때, CPU 기반 모형 대비 GPU 기반 모형의 연산 효율이 최대 약 150배 증가하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.1
• /
• pp.1-10
• /
• 2015

In this paper, we propose an efficient parallel implementation method of a widely used complex four-stage fire detection algorithm using a graphics processing unit (GPU) to improve the performance of the algorithm and analyze the performance of the parallel implementation method. In addition, we use seven different resolution videos (QVGA, VGA, SVGA, XGA, SXGA+, UXGA, QXGA) as inputs of the four-stage fire detection algorithm. Moreover, we compare the performance of the GPU-based approach with that of the CPU implementation for each different resolution video. Experimental results using five different fire videos with seven different resolutions indicate that the execution time of the proposed GPU implementation outperforms that of the CPU implementation in terms of execution time and takes a 25.11ms per frame for the UXGA resolution video, satisfying real-time processing (30 frames per second, 30fps) of the fire detection algorithm.

• Journal of the Korea Computer Graphics Society
• /
• v.20 no.2
• /
• pp.13-24
• /
• 2014

In this study, a graphics-processing-unit (GPU)-based acceleration technique is proposed for the feature-based image morphing. This technique uses the depth-buffer of the graphics hardware to calculate efficiently the shortest distance between a pixel and the control lines. The pairs of control lines between the source image and the destination image are determined by user's input, and the distance function of each control line is rendered using two rectangles and two cones. The distance between each pixel and its nearest control line is stored in the depth buffer through the graphics pipeline, and this is used to conduct the morphing operation efficiently. The pixel-unit morphing operation is parallelized using the compute unified device architecture (CUDA) to reduce the morphing time. We demonstrate the efficiency of the proposed technique using several experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.12
• /
• pp.2355-2362
• /
• 2016

Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.