• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.507-512
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• 2014

Recently, Distributed computing processing begins using both CPU(Central processing unit) and GPU(Graphic processing unit) to improve the performance to overcome darksilicon problem which cannot use all of the transistors because of the electric power limitation. There is an integrated graphics processor that CPU and GPU share memory and Last level cache(LLC). But, There is no LLC access rules between CPU and GPU, so if GPU and CPU processes run together at the same time, performance of both processes gets worse because of the contention on the LLC. This Paper gives evidence to prove the need of the Cache Partitioning and is mentioned about the cache partitioning design using page coloring to allocate the L3 Cache space only for the GPU process to guarantee GPU process performance.

• Electronics and Telecommunications Trends
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• v.28 no.2
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• pp.50-57
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• 2013

스마트폰 및 태블릿 PC에 들어가는 핵심 부품인 AP(Application Processor)는 모두 GPU(Graphics Processing Unit)를 내장하고 있다. 이는 칩 면적의 제약과 사용 가능한 전력의 한계로 데스크톱의 그래픽 카드에 탑재된 고성능 GPU와는 다른 설계 제약을 받는다. 본고에서는 고성능 GPU와 다른 설계 조건을 갖는 mobile GPU 기술에 대해서 알아보았고 대표적인 commercial mobile GPU인 Imagination, ARM, Qualcomm, NVidia사의 mobile GPU의 특징 및 성능에 대해서 알아보았다.

• Journal of the Korea Computer Graphics Society
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• v.23 no.2
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• pp.1-10
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• 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 the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.956-962
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• 2020

Recently, as the size of data used in an embedded system increases, the need for an ECC decoding operation to robustly receive a massive data is emphasized. In this paper, we propose a method to accelerate the execution of computations that derive syndrome vectors when ECC decoding is performed using Hamming code in an embedded system with a built-in GPU. The proposed acceleration method uses the matrix-vector multiplication of the decoding operation using the CSR format, one of the data structures representing sparse matrix, and is performed in parallel in the CUDA kernel of the GPU. We evaluated the proposed method using a target embedded board with a GPU, and the result shows that the execution time is reduced when ECC decoding operation accelerated based on the GPU than used only CPU.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.75-78
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• 2022

Deep Neural Networks (DNN) has become an essential data processing architecture for the implementation of multiple computer vision tasks. Recently, DNN-based algorithms achieve much higher recognition accuracy than traditional algorithms based on shallow learning. However, training and inference DNNs require huge computational capabilities than daily usage purposes of computers. Moreover, with increased size and depth of DNNs, CPUs may be unsatisfactory since they use serial processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. In this paper, we analyze the inference time complexity of DNNs using well-known computer vision library, OpenCV. We measure and analyze inference time complexity for three cases, CPU, GPU-Float32, and GPU-Float16.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.11-19
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• 2020

Modern GPU can execute general purpose computation on the graphic processing unit, and provide high performance by exploiting many core on GPU. To run AES algorithm efficiently, parallel computational resources are required. However, computational resource of CPU architecture are not enough to cryptographic algorithm such as AES whereas GPU architecture has mass parallel computation resources. Therefore, this paper reduce the time to execute AES by employing parallel computational resource on GPGPU. Unfortunately, AES cannot utilize computational resource on GPGPU since it isn't suitable to GPGPU architecture. In this paper, IPC based dynamic SM management technique are proposed to efficiently execute AES on GPGPU. IPC based dynamic SM management can increase and decrease the number of active SMs by using IPC in run-time. According to simulation results, proposed technique improve the performance by increasing resource utilization compared to baseline GPGPU architecture. The results show that AES improve the performance by 41.2% on average.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.153-161
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• 2018

This paper describes the performance improvement of Software (SW) digital filter using GPU (Graphical Processing Unit). The previous developed SW digital filter has a problem that it operates on a CPU (Central Processing Unit) basis and has a slow speed. The GPU was introduced to filter the data of the EAVN (East Asian VLBI Network) observation to improve the operation speed and to process data with other stations through filtering, respectively. In order to enhance the computational speed of the SW digital filter, NVIDIA Titan V GPU board with built-in Tensor Core is used. The processing speed of about 0.78 (1Gbps, 16MHz BW, 16-IF) and 1.1 (2Gbps, 32MHz BW, 16-IF) times for the observing time was achieved by filtering the 95 second observation data of 2 Gbps (512 MHz BW, 1-IF), respectively. In addition, 2Gbps data is digitally filtered for the 1 and 2Gbps simultaneously observed with KVN (Korean VLBI Network), and compared with the 1Gbps, we obtained similar values such as cross power spectrum, phase, and SNR (Signal to Noise Ratio). As a result, the effectiveness of developed SW digital filter using GPU in this research was confirmed for utilizing the data processing and analysis. In the future, it is expected that the observation data will be able to be filtered in real time when the distributed processing optimization of source code for using multiple GPU boards.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.540-546
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• 2010

Since the stereoscopic 3-dimensional (3D) video that provides users with a realistic multimedia service requires twice as much data as 2-dimensional (2D) video, it is difficult to construct the fast system. In this paper, we propose a fast stereoscopic 3D broadcasting system based on the depth information. Before the transmission, we encode the input 2D+depth video using x264, an open source H.264/AVC fast encoder to reduce the size of the data. At the receiver, we decode the transmitted bitstream in real time using a compute unified device architecture (CUDA) video decoder API on NVIDIA graphics processing unit (GPU). Then, we apply a fast view synthesis method that generates the virtual view using GPU. The proposed system can display the output video in both 2DTV and 3DTV. From the experiment, we verified that the proposed system can service the stereoscopic 3D contents in 24 frames per second at most.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.624-631
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• 2012

For many medical imaging systems, volume datasets are stored as a compressed form, so that the dataset has to be decompressed before it is visualized. Since the decompression process takes quite a long time, we present an acceleration method for medical volume decompression using GPU. Our method supports that both lossy and lossless compression and progressive refinement is possible to satisfy variable user requirements. Moreover, our decompression method is well parallelized for GPU so that the decompression takes a very short time. Finally, we designed that the decompression and volume rendering work in one framework so that the selective decompression is available. As a result, we gained additional improvement in volume decompression.