• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.199-203
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• 2013

In this paper, a fast algorithm of discrete cosine transform-based interpolation filter (DCT-IF) for HEVC (high efficiency video coding) encoder is proposed. DCT-IF filter accounts for around 30% of encoder complexity, according to the computational complexity analysis with the HEVC reference software. In this work, the proposed DCT-IF is optimized by applying frame-level interpolation, SIMD optimization, and task-level parallelization via OpenMP on a developed C-based HEVC encoder. Performance analysis is conducted by measuring speed-up factor of the proposed optimization technique on the developed encoder. The results show that speed-up factors by frame-level interpolation, SIMD, and OpenMP are approximately 38-46, 3.6-4.4, and 3.0-3.7, respectively. In the end, we achieved the speed-up factor of 498.4 with the proposed fast algorithm.

• Journal of The Korean Astronomical Society
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• v.49 no.4
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• pp.157-162
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• 2016

High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256×256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.445-452
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• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.488-495
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• 2009

This paper presents a fast feature extraction method for autonomous mobile robots utilizing parallel processing and based on OpenMP, SSE (Streaming SIMD Extension) and CUDA programming. In the first step on CPU version, the algorithms and codes are optimized and then implemented by parallel processing. The parallel algorithms are debugged to maintain the same level of performance and the process for extracting key points and obtaining dominant orientation with respect to key points is parallelized. After extraction, a parallel descriptor via SSE instructions is constructed. And the GPU version also implemented by parallel processing using CUDA based on the SIFT. The GPU-Parallel descriptor achieves an acceleration up to five times compared with the CPU-Parallel descriptor, but it shows the lower performance than CPU version. CPU version also speed-up the four and half times compared with the original SIFT while maintaining robust performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.211-216
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• 2009

As demands for high-definition television (HDTV) increase, the implementation of real-time decoding of high-definition (HD) video becomes an important issue. The data size for HD video is so large that real-time processing of the data is difficult to implement, especially with software. In order to implement a fast moving picture expert group-2 decoder for HDTV, we compose five scenarios that use parallel processing techniques such as data decomposition, task decomposition, and pipelining. Assuming the multi digital signal processor environments, we analyze each scenario in three aspects: decoding speed, L1 memory size, and bandwidth. By comparing the scenarios, we decide the most suitable cases for different situations. We simulate the scenarios in the dual-core and dual-central processing unit environment by using OpenMP and analyze the simulation results.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.2
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• pp.57-65
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• 2016

In this paper, we propose a system for efficient use of shared memory between CPU and GPU. The system, called Fusion Architecture, assures consistency of the shared memory and minimizes cache misses that frequently occurs on Heterogeneous System Architecture or Unified Virtual Memory based systems. It also maximizes the performance for memory intensive jobs by efficient allocation of GPU cores. To test between architectures on various scenarios, we introduce the Fusion Architecture Analyzer, which compares OpenMP, OpenCL, CUDA, and the proposed architecture in terms of memory overhead and process time. As a result, Proposed fusion architectures show that the Fusion Architecture runs benchmarks 55% faster and reduces memory overheads by 220% in average.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.2
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• pp.102-110
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• 2006

This paper explains the design and development of the stand-alone high sound quality Internet Radio system, which is aimed for a small embedded type audio device rather than a general PC type. This device is designed to work with an Internet connection. This kind of system is not standardized so far, and also the related algorithm is not open to the public. So it is necessary to analyze several receiving algorithms of current radio receivers, and develop our own hardware in order to overcome these obstacles, finally to get the high quality of sound radio. The main electronic components of this Internet Radio are TCP/IP interfaces, an audio MP3 decoder, an I/O interface, and a Flash Memory Card with advanced audio multicasting for the next-generation Internet Radio. Basic structures and implementation issues of the next-generation most-versatile digital music player, and Internet Radio receivers, are discussed.

• 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 IKEEE
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• v.17 no.1
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• pp.51-56
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• 2013

This thesis propose a improved matching method for implementing an ORB algorithm based multi-object recognition. SURF algorithm that is well known in the object recognition algorithms is robust in object recognition. However, there is a disadvantage for real time operation because, SURF implemention requires a lot of computation. Therefore we propose a modified ORB algorithm which shows the result of almost 70% speed improvement by improving matching part to recognize multi object on real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.728-741
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• 2015

Stereo vision has become an important technical issue in the field of 3D imaging, machine vision, robotics, image analysis, and so on. The depth map extraction from stereo video is a key technology of stereoscopic 3D video requiring stereo correspondence algorithms. This is the matching process of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since it requires a lot of computational power, there are significant speed-performance advantages when exploiting parallel processing available on processors. In this situation, multi-core CPU may allow many parallel programming technologies to be realized in users computing devices. This paper proposes parallel implementations for calculating disparity map using a shared memory programming and exploiting the streaming SIMD extension technology. By doing so, we can take advantage both of the hardware and software features of multi-core processor. For the performance evaluation, we implemented a parallel SAD algorithm with OpenMP and SSE2. Their processing speeds are compared with non parallel version on stereoscopic streaming video. The experimental results show that both technologies have a significant effect on the performance and achieve great improvements on processing speed.