• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.17-24
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• 2009

More recently, the Green Computing have become a important issue in all fields of industry. The energy efficiency cannot be over-emphasized. Microprocessor companies such as Intel Corporation design processors with taking both energy efficiency and performance into account. Nevertheless, general computer users typically utilize the CPU overclocking to enhance the application performance. The overclocking is traditionally considered as an evil in terms of the power consumption. In this paper, we present effective CPU overclocking schemes, which raise CPU frequency while keeping current CPU supply voltage for energy reduction and performance improvement. The proposed scheme gain both energy reduction and performance improvement. Evaluation results show that our proposed schemes reduce the processor execution time as much as 17% and total computer system energy as much as 5%, respectively. In addition, our effective CPU overclocking schemes reduce the Energy Delay Product (EDP) as much as 22%, on average.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.393-397
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• 2005

When a real-time image processing application is implemented with a general-purpose computer, CPU (Central Processing Unit) is usually heavily loaded and in many cases that CPU alone cannot meet the real-time requirement at all. Most modern computers are equipped with powerful Graphics Processing Units (GPUs) to accelerate graphics operations. There is a trend that the power of GPU outgrows that of CPU. If we take advantage of the powerful GPU for more general operations other than pure graphics operations, the processing time can be reduced. In this study, we will present techniques that apply GPU to general operations such as image processing procedures. Our experiment results show that significant speed-up can be achieved by using GPU.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.21-30
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• 2016

Risk management system should be able to support a decision making within a short time to analyze stream data in real time. Many analytical systems consist of CPU computation and disk based database. However, it is more problematic when existing system analyzes stream data in real time. Stream data has various production periods from 1ms to 1 hour, 1day. One sensor generates small data but tens of thousands sensors generate huge amount of data. If hundreds of thousands sensors generate 1GB data per second, CPU based system cannot analyze the data in real time. For this reason, it requires fast processing speed and scalability for analyze stream data. In this paper, we present a fast visualization technique that consists of hybrid database and GPU computation. In order to evaluate our technique, we demonstrate a visual analytics system that analyzes pipeline leak using sensor and tweet data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.57-64
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• 2012

Multiplayer, real-time games are a kind of soft real-time systems because a game server has to respond to requests from many clients within specified time constraints. Client events have different timeliness and consistency requirements according to their nature in the game world. These requirements lead to different priorities on CPU processing. Events can be divided into different groups, depending on their consistency degree and priority. To handle these events with different priority and meet their timing constraints, we propose a priority-based group task scheduling policy in this paper. The number of clients or events requested by each client may be increased temporarily. In the presence of transient overloading, the game server needs to allocate more CPU bandwidth to serve an event with the higher priority level preferentially. The proposed scheduling policy is capable of enhancing real-time performance of the entire system by maximizing the number of events with higher priority completed successfully within their deadlines. The performance of this policy is evaluated through extensive simulation experiments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.1
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• pp.41-52
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• 1989

Numerical characteristics for the shallow water equation are analyzed with ADI, Hansen, Heaps, Richtmyer and MacCormack schemes. Stability, CPU time and accuracy are investigated for the linear model which has analytic solutions and circulation is simulated for the nonlinear model. The results show that ADI method has some defects in CPU time and accuracy for the computation of velocity. But ADI method simulates circulation well and has the largest stability region. Richtmyer scheme is the best among the other explicit schemes. Effective viscosity term is found to be essential for numerical experiments of the shallow water equation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.38-44
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• 2003

This paper describes the development of stand-alone image processing module on Strong Arm CPU employing an embedded Linux. Stand-alone image Processing module performs various functions such as thresholding, edge detection, and image enhancement of a raw image data in real time. The comparison of execution time between similar PC and developed module shows the satisfactory results. This Paper provides the possibility of applying embedded Linux successfully in industrial devices.

• 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.

• 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.

• International Journal of Contents
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• v.13 no.4
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• pp.39-46
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• 2017

Due to resource constraints, most of Android application developers need to address potential performance problems during application development and maintenance. The coding styles and patterns of Android programming could often affect the execution time and energy efficiency which are utilized by the Android applications. Thus, it is necessary for application developers to apply performance-enhancing programming practices for mobile application development. This paper introduces performance-enhancing best practices for Android programming, and further, it evaluates the impact of these practices on the CPU time of the application. The original version with the performance-worsening code has been refactored to become an efficient version without changing its functionality. To demonstrate the efficiency of the proposed approach, each coding pattern was evaluated by measuring the CPU time under the controlled runtime environment. Furthermore, the Android applications were evaluated and compared via the CPU time of the original version, with that of the refactored version. These experimental results indicate that, by -using the proposed programming practices, the Android developer can develop performance-efficient mobile applications.

• Journal of IKEEE
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• v.27 no.3
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• pp.265-272
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• 2023

In this paper, a GPU-accelerated kernel of range Doppler algorithm (RDA) was developed for real-time image formation based on frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR). A pinned memory was used to minimize the data transfer time between the host and the GPU device, and the kernel was configured to perform all RDA operations on the GPU to minimize the number of data transfers. The dataset was obtained through the FMCW drone SAR experiment, and the GPU acceleration effect was measured in an intel i7-9700K CPU, 32GB RAM, and Nvidia RTX 3090 GPU environment. Including the data transfer time between host and devices, it was measured to be accelerated up to 3.41 times compared to the CPU, and when only the acceleration effect of operation was measured without including the data transfer time, it was confirmed that it could be accelerated up to 156 times.