• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.2
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• pp.87-93
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• 2013

In this paper, we implemented and evaluated the performance of a vector-based rasterization algorithm of 3D graphics using a SIMD-based many-core processor that consists of 4,096 processing elements. In addition, we compared the performance and efficiency of the rasterization algorithm using the many-core processor and commercial GPU (Graphics Processing Unit) system which consists of 7 GPUs and each of which have 512 cores. Experimental results showed that the SIMD-based many-core processor outperforms the commercial GPU system in terms of execution time (3.13x speedup), energy efficiency (17.5x better), and area efficiency (13.3x better). These results demonstrate that the SIMD-based many-core processor has potential as an embedded mobile processor.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.1-10
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• 2013

In this paper, we implement the SIFT(Scale-Invariant Feature Transform) algorithm for feature point extraction using a many-core processor, and analyze the performance, area efficiency, and system area efficiency of the many-core processor. In addition, we demonstrate the potential of the proposed many-core processor by comparing the performance of the many-core processor with that of high-performance CPU and GPU(Graphics Processing Unit). Experimental results indicate that the accuracy result of the SIFT algorithm using the many-core processor was same as that of OpenCV. In addition, the many-core processor outperforms CPU and GPU in terms of execution time. Moreover, this paper proposed an optimal model of the SIFT algorithm on the many-core processor by analyzing energy efficiency and area efficiency for different octave sizes.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.281-286
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• 2011

Data sorting is one of important pre-process to utilize huge data in modern society, but sorting spends a lot of time by sorting itself. In this paper, we presented hybrid sorting algorithm that splits array to sort concurrently in CPU and GPU. To do this, we decided most effective range of array based on hardware performance, then accomplished reducing whole sorting time by concurrent sorting on CPU and GPU. As shown in results of experiment, hybrid sorting improved about eight percent of sorting time in comparison with the sorting time using only GPU.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.775-787
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• 2010

Unlike general-purpose CPUs, the GPUs have been specialized as many-core streaming processors, and are frequently replacing the CPUs in an increasing range of computations thanks to their outstanding parallel computing capacity. In order to respond to such trend, NVIDIA has recently issued a new parallel computing architecture called CUDA(Compute Unified Device Architecture), offering a flexible GPU programming environment for GPGPU(General Purpose GPU) computing. In general, when programmers use the CUDA API, they should clearly understand many aspects of GPU's computing architecture to produce efficient parallel software. In this article, we explain several optimization techniques for CUDA programming that we have verified through a lot of experiment and trial and error, and review how those techniques affect the performance of code execution. In particular, we use a specific problem as an example to analyze several elements that affect performances, such as effective accesses to hierarchical memory system, processor occupancy, and latency hiding. In conclusion, we present several directions that may be utilized effectively in CUDA-based parallel programming.

• Journal of Information Processing Systems
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• v.8 no.1
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• pp.159-174
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• 2012

This paper presents a study on a high-performance design for a block cipher algorithm implemented on modern many-core graphics processing units (GPUs). The recent emergence of VLSI technology makes it feasible to fabricate multiple processing cores on a single chip and enables general-purpose computation on a GPU (GPGPU). The GPU strategy offers significant performance improvements for all-purpose computation and can be used to support a broad variety of applications, including cryptography. We have proposed an efficient implementation of the encryption/decryption operations of a block cipher algorithm, SEED, on off-the-shelf NVIDIA many-core graphics processors. In a thorough experiment, we achieved high performance that is capable of supporting a high network speed of up to 9.5 Gbps on an NVIDIA GTX285 system (which has 240 processing cores). Our implementation provides up to 4.75 times higher performance in terms of encoding and decoding throughput as compared to the Intel 8-core system.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.253-260
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• 2009

Many algorithms for computer vision and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation has two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job that needs much cooperation between CPU and GPU, which is usual in image processing and pattern recognition contrary to the graphic area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results in effectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text extraction system using the proposed architecture, and the computational times showed about 15 times faster than implementation on only GPU without OpenMP.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.37-45
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• 2018

The current system upon which a variety of programs are in operation has continuously expanded its domain from conventional single-core and multi-core system to many-core and heterogeneous system. However, existing researches have focused mostly on parallelizing programs based CUDA framework and rarely on AMD based GCN-GPU optimization. In light of the aforementioned problems, our study focuses on the optimization techniques of the GCN architecture in a GPGPU environment and achieves a performance improvement. Specifically, by using performance techniques we propose, we have reduced more then 30% of the computation time of matrix multiplication and convolution algorithm in GPGPU. Also, we increase the kernel throughput by more then 40%.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.173-180
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• 2011

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

• 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.