• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.8
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• pp.1-9
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• 2012

Quantum-Inspired Evolutionary Algorithm(QEA) contains sufficient data-level parallelism to be naturally accelerated on GPUs. For an efficient reduction of execution time, however, careful task-mapping should be done to properly reflect the characteristics of CPU and GPU. Furthermore, when deciding which part of the application should run on GPU, we need to consider the data transfer between CPU and GPU memory spaces as well as the data-level parallelism. In addition, the usage of zero-copy host memory, proper choice of the execution configuration, and thread organization considering memory coalescing is important to further reduce the execution time. With all these techniques, we could run QEA 3.69 times faster on average in comparison with the multi-threading CPU for the case of 0-1 knapsack problem with 30,000 items.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.45-52
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• 2006

This paper presents an image-space algorithm to real-time collision detection, which is run completely by GPU. For a single object or for multiple objects with no collision, the front and back faces appear alternately along the view direction. However, such alternation is violated when objects collide. Based on these observations, the algorithm propose the depth peeling method which renders the minimal surface of objects, not whole surface, to find colliding. The Depth peeling method utilizes the state-of-the-art functionalities of GPU such as framebuffer object, vertexbuffer object, and occlusion query. Combining these functions, multi-pass rendering and context switch can be done with low overhead. Therefore proposed approach has less rendering times and rendering overhead than previous image-space collision detection. The algorithm can handle deformable objects and complex objects, and its precision is governed by the resolution of the render-target-texture. The experimental results show the feasibility of GPU-based collision detection and its performance gain in real-time applications such as 3D games.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.736-738
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• 2004

신경망의 테스트 단계를 실시간으로 처리하기 위해 많은 노력이 있었다 본 논문은 일반적인 그래픽스 하드웨어를 이용하여 더욱 빠른 신경망을 구현하고, 구현된 시스템을 영상 처리 분야에 적용함으로써 효용성을 검증한다. GPU는 CPU보다 병렬연산에 효과적이다. GPU의 병렬성을 효율적으로 사용하기 위하여, 다수의 신경망 입력벡터와 웨이트벡터를 모아서 많은 내적연산을 하나의 행렬곱 연산으로 대체하였고, 시그모이드와 바이어스 항 덧셈 연산도 픽셀세이더로 병렬 구현하였다. ATI RADEON 9800 XT 보드를 이용하여 구현된 신경망 시스템은 CPU를 사용한 기존의 시스템과 비교하여 정악도의 차이 없이 30배 정도의 속도 향상을 얻을 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.355-357
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• 2012

차세대 웹 브라우저는 멀티 쓰레드(multi-thread) 구조로 되어 있으며 HTML5와 WebGL을 기반으로 화려한 그래픽을 구사하기 때문에, 멀티 코어(multi-core) CPU와 GPU의 성능이 웹 브라우저의 성능에 큰 영향을 미치고 있다. 본 논문은 오픈 소스 웹 브라우저인 크로미엄(Chromium) 상에서 프로세서의 성능 변화에 따라 웹 브라우저에서 실행되는 웹 어플리케이션의 성능이 어떤 양상으로 변화하는지와 이 변화에 웹 브라우저의 각 동작이 얼마나 기여하는지를 비교 분석하였다. 그 결과 CPU 코어의 수가 렌더링 성능에 큰 영향을 주며, GPU의 성능은 WebGL의 성능을 크게 좌우함을 알 수 있었다.

• Journal of IKEEE
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• v.22 no.2
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• pp.281-288
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• 2018

In this paper, we propose an intelligent face recognition and tracking system that distributes GPU resources using CUDA. The proposed system consists of five steps such as GPU allocation algorithm that distributes GPU resources in optimal state, face area detection and face recognition using deep learning, real time face tracking, and PTZ camera control. The GPU allocation algorithm that distributes multi-GPU resources optimally distributes the GPU resources flexibly according to the activation level of the GPU, unlike the method of allocating the GPU to the thread fixedly. Thus, there is a feature that enables stable and efficient use of multiple GPUs. In order to evaluate the performance of the proposed system, we compared the proposed system with the non - distributed system. As a result, the system which did not allocate the resource showed unstable operation, but the proposed system showed stable resource utilization because it was operated stably. Thus, the utility of the proposed system has been demonstrated.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.250-253
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• 2011

Chip manufacture nowadays turned their attention from accelerating uniprocessors to integrating multiple cores on a chip. Moreover desktop graphic hardware is now starting to support general purpose computation. Desktop users are able to use multi-core CPU and GPU as a high performance computing resources these days. However exploiting parallel computing resources are still challenging because of lack of higher programming abstraction for parallel programming. The 2-dimensional discrete cosine transform (2D-DCT) algorithms are most computational intensive part of JPEG encoding. There are many fast 2D-DCT algorithms already studied. We implemented several algorithms and estimated its runtime on multi-core CPU and GPU environments. Experiments show that data parallelism can be fully exploited on CPU and GPU architecture. We expect parallelized DCT bring performance benefit towards its applications such as JPEG and MPEG.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.1
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• pp.17-23
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• 2014

Graphics processing units (GPUs) are ideal for solving problems involving parallel data computations. In this study, the GPU is used for effectively carrying out a multi-body dynamic simulation with particle dynamics. The Hilber-Hushes-Taylor (HHT) implicit integration algorithm is used to solve the integral equations. For detecting collisions among particles, the spatial subdivision algorithm and discrete-element methods (DEM) are employed. The developed program is verified by comparing its results with those of ADAMS. The numerical efficiencies of the serial program using the CPU and the parallel program using the GPU are compared in terms of the number of particles, and it is observed that when the number of particles is greater, more computing time is saved by using the GPU. In the present example, when the number of particles is 1,300, the computational speed of the parallel analysis program is about 5 times faster than that of the serial analysis program.

• Smart Media Journal
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• v.8 no.2
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• pp.9-15
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• 2019

Since the computational complexity for hologram generation increases exponentially with respect to the size of the point cloud, parallel processing using CUDA and/or OpenCL library based on multiple GPUs has recently become popular. The CUDA kernel for parallelization needs to consist of threads, blocks, and grids properly in accordance with the number of cores and the memory size in the GPU. In addition, in case of multiple GPU environments, the distribution in grid-by-grid, in block-by-block, or in thread-by-thread is needed according to the number of GPUs. In order to evaluate the performance of CGH generation, we compared the computational speed in CPU, in single GPU, and in multi-GPU environments by gradually increasing the number of points in a point cloud from 10 to 1,000,000. We also present a memory structure design and a calculation method required in the CUDA-based parallel processing to accelerate the CGH (Computer Generated Hologram) generation operation in multiple GPU environments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.27-35
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• 2015

Increasing the number of GPU (Graphic Processor Unit) cores, the studies on High Performance Computing Platform using GPU have actively been made in recent. This trend has led to the development of GPGPU (General Purpose GPU) and CUDA (Compute Unified Device Architecture) Framework. In this paper, we explain the many benefits of the GPU based system, and propose the ICIDF(Identify Compute-Intensive Data set and Function) methodology to apply GPU technology to legacy military information system for performance improvement. To demonstrate the efficiency of this methodology, we applied this method to AES CPU based program obtained from the Internet web site. Simply changing the data structure made improved the performance of AES program. As a result, the performance of AES based GPU program is improved gradually up to 10 times. Depending on the developer's ability, additional performance improvement can be expected. The problem to be solved is heat issue, but this problem has been much improved by the development of the cooling technology.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.48-54
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• 2013

In these days, the state-of-art technologies employ the heterogeneous parallelization of CPU and GPU for fluid simulations in the field of computer graphics. In this paper, we present a novel CPU-GPU parallel algorithm that solves projection step of fluid simulation more efficiently than existing sequential CPU-GPU processing. Fluid simulation that requires high computational resources can be carried out efficiently by the proposed method.