• Journal of KIISE:Databases
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• v.37 no.5
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• pp.275-284
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• 2010

GPUs are stream processors based on multi-cores, which can process large data with a high speed and a large memory bandwidth. Furthermore, GPUs are less expensive than multi-core CPUs. Recently, usage of GPUs in general purpose computing has been wide spread. The CUDA architecture from Nvidia is one of efforts to help developers use GPUs in their application domains. In this paper, we propose techniques to parallelize a skyline algorithm which uses a simple nested loop structure. In order to employ the CUDA programming model, we apply our optimization techniques to make our skyline algorithm fit into the performance restrictions of the CUDA architecture. According to our experimental results, we improve the original skyline algorithm by 80% with our optimization techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1457-1470
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• 2015

In wireless multimedia sensor networks (WMSN), the latency of transmission is an increasingly problem. With the improvement of resolution, the time cost in image and video compression is more and more, which seriously affects the real-time of WMSN. In JPEG system, the core of the system is DCT, but DCT-JPEG is not the best choice. Block-based DCT transform coding has serious blocking artifacts when the image is highly compressed at low bit rates. APBT is used in this paper to solve that problem, but APBT does not have a fast algorithm. In this paper, we analyze the structure in JPEG and propose a parallel framework to speed up the algorithm of JPEG on GPU. And we use all phase biorthogonal transform (APBT) to replace the discrete cosine transform (DCT) for the better performance of reconstructed image. Therefore, parallel APBT-JPEG is proposed to solve the real-time of WMSN and the blocking artifacts in DCT-JPEG in this paper. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of APBT-JPEG. Experimental results show that the maximum speedup ratio of parallel algorithm of APBT-JPEG can reach more than 100 times with a very low version GPU, compared with conventional serial APBT-JPEG. And the reconstructed image using the proposed algorithm has better performance than the DCT-JPEG in terms of objective quality and subjective effect. The proposed parallel algorithm based on GPU of APBT also can be used in image compression, video compression, the edge detection and some other fields of image processing.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.55-62
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• 2015

In this study we demonstrate ultrahigh-resolution spectral domain optical coherence tomography (UHR SD-OCT) with a linear-wavenumber (k) spectrometer, to accelerate signal processing and to display two-dimensional (2-D) images in real time. First, we performed a numerical simulation to find the optimal parameters for the linear-k spectrometer to achieve ultrahigh axial resolution, such as the number of grooves in a grating, the material for a dispersive prism, and the rotational angle between the grating and the dispersive prism. We found that a grating with 1200 grooves and an F2 equilateral prism at a rotational angle of $26.07^{\circ}$, in combination with a lens of focal length 85.1 mm, are suitable for UHR SD-OCT with the imaging depth range (limited by spectrometer resolution) set at 2.0 mm. As guided by the simulation results, we constructed the linear-k spectrometer needed to implement a UHR SD-OCT. The actual imaging depth range was measured to be approximately 2.1 mm, and axial resolution of $3.8{\mu}m$ in air was achieved, corresponding to $2.8{\mu}m$ in tissue (n = 1.35). The sensitivity was -91 dB with -10 dB roll-off at 1.5 mm depth. We demonstrated a 128.2 fps acquisition rate for OCT images with 800 lines/frame, by taking advantage of NVIDIA's compute unified device architecture (CUDA) technology, which allowed for real-time signal processing compatible with the speed of the spectrometer's data acquisition.

• Journal of The Korean Astronomical Society
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• v.51 no.3
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• pp.65-71
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• 2018

The near real-time speckle masking reconstruction technique has been developed to accelerate the processing of solar images to achieve high resolutions for ground-based solar telescopes. However, the reconstruction of solar subimages in such a speckle reconstruction is very time-consuming. We design and implement a new parallel speckle masking reconstruction algorithm based on the Compute Unified Device Architecture (CUDA) on General Purpose Graphics Processing Units (GPGPU). Tests are performed to validate the correctness of our program on NVIDIA GPGPU. Details of several parallel reconstruction steps are presented, and the parallel implementation between various modules shows a significant speed increase compared to the previous serial implementations. In addition, we present a comparison of runtimes across serial programs, the OpenMP-based method, and the new parallel method. The new parallel method shows a clear advantage for large scale data processing, and a speedup of around 9 to 10 is achieved in reconstructing one solar subimage of $256{\times}256pixels$. The speedup performance of the new parallel method exceeds that of OpenMP-based method overall. We conclude that the new parallel method would be of value, and contribute to real-time reconstruction of an entire solar image.

• Journal of Korea Game Society
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• v.11 no.1
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• pp.147-157
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• 2011

Webcam doesn't perform memory-alignment in order to reduce the transmission time of image data. Memory-unaligned image data is unsuitable for the processing on GPU. Accordingly, we convert it to available color format for optimization in high speed image processing. In this paper, we propose a technique that accelerates webcam's color format conversion by using NVDIA CUDA. We propose an optimization which is about memory accesses and thread composition, also evaluate memory and computing performance for verifying a hypothesis which is the performance of the proposed architecture and optimizing degree on low-performance GPU. Following the optimization technique, we show performance improvements over maximum 68 percent.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.707-711
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• 2010

Most relational database systems provide $B^+$-trees as their main index structures, and use bulk-loading techniques for creating new $B^+$-trees on existing data from scratch. Although bulk loadings are more effective than inserting keys one by one, they are still time-consuming because they have to sort all the keys from large data. To improve the performance of bulk loadings, this paper proposes an efficient parallel bulk loading method for $B^+$-trees based on CUDA, which is a parallel computing architecture developed by NVIDIA to utilize computing powers of graphic processor units for general purpose computing. Experimental results show that the proposed method enhance the performance more than 70 percents compared to existing bulk loading methods.

• Smart Media Journal
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• v.5 no.4
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• pp.69-74
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• 2016

Recently, parallel computing using GPGPU(General-Purpose computing on Graphics Processing Units) according to the development of the graphics processing unit is expanding. This can be achieved through the processing speeds faster than traditional computing environments across many fields, including science, medicine, engineering, and analysis. However, in using the GPU technology to implement the a parallel program there are many constraints. In this paper, we port a CPU-based program(Video Quality Measurement Program) to use technology. The program ported to GPU-based show about 1.83 times the execution speed than CPU-based program. We study on the acceleration of the GPU-based program. Also we discuss the technical constraints and problems that occur when you modify the CPU to the GPU-based programs.

• 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.

• Spatial Information Research
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• v.17 no.3
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• pp.371-379
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• 2009

Recently, GPU (Graphics Processing Unit) has been improved rapidly on the need of speed for gaming. As a result, GPU contains multiple ALU (Arithmetic Logic Unit) for parallel processing of a lot of graphics data, such as transform, ray tracing, etc. Therefore, this paper proposed a technique for parallel processing of spatial data using GPU. Spatial data consists of multiple coordinates, and each coordinate contains value of x and y axis. To display spatial data graphics operations have to be processed to large amount of coordinates. Because the graphics operation is identical and coordinates are multiple data, SIMD (Single Instruction Multiple Data) parallel processing of GPU can be used for processing of spatial data to improve performance. This paper implemented SIMD parallel processing of spatial data using two kinds of SDK (Software Development Kit). CUDA and ATI Stream are used for NVIDIA and ATI GPU respectively. Experiments that measure time of calculation for graphics operations are carried out to observe enhancement of performance. Experimental result is reported that proposed method can enhance performance up to 1,162% for graphics operations. The proposed method that uses parallel processing with GPU for spatial data can be generally used to enhance performance for applications which deal with large amount of spatial data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.955-961
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• 2017

In this paper, we propose a parallel approximate string matching algorithm with k-mismatches for multiple fixed-length patterns (PMASM) in DNA sequences. PMASM is developed from parallel single pattern approximate string matching algorithms to effectively calculate the Hamming distances for multiple patterns with a fixed-length. In the preprocessing phase of PMASM, all target patterns are binary encoded and stored into a look-up memory. With each input character from the input string, the Hamming distances between a substring and all patterns can be updated at the same time based on the binary encoding information in the look-up memory. Moreover, PMASM adopts graphics processing units (GPUs) to process the data computations in parallel. This paper presents three kinds of PMASM implementation methods in GPUs: thread PMASM, block-thread PMASM, and shared-mem PMASM methods. The shared-mem PMASM method gives an example to effectively make use of the GPU parallel capacity. Moreover, it also exploits special features of the CUDA (Compute Unified Device Architecture) memory structure to optimize the performance. In the experiments with DNA sequences, the proposed PMASM on GPU is 385, 77, and 64 times faster than the traditional naive algorithm, the shift-add algorithm and the single thread PMASM implementation on CPU. With the same NVIDIA GPU model, the performance of the proposed approach is enhanced up to 44% and 21%, compared with the naive, and the shift-add algorithms.