• Journal of Aerospace System Engineering
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• v.13 no.5
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• pp.57-64
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• 2019

Recently, avionics systems are being developed as integrated modular architecture applying the modular integration design of the functional unit to reduce maintenance costs and increase operating performance. Additionally, a partitioning operating system based on virtualization technology was used to process various mission control functions. In virtualization technology, the CPU processing load distribution is a key consideration. Especially, the uncertainty of the I/O processing time is a risk factor in the design of reliable avionics systems. In this paper, we examine the influence of the I/O processing method by comparing and analyzing the CPU processing load by the I/O processing method through use of case analysis and applying it to the example of spatial-temporal partitioning.

• Journal of Korea Game Society
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• v.9 no.2
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• pp.135-142
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• 2009

Mesh denoising is a method to remove noise applying various filters. However, those methods usually spend much time since filtering is performed on CPU. Because GPU is specialized for floating point operations and faster than CPU, real-time processing for complex operations is possible. Especially mesh denoising is adequate for GPU parallel processing since it repeats the same operations for vertices or triangles. In this paper, we propose mesh denoising algorithm based on bilateral filtering using GPU parallel processing to reduce processing time. It finds neighbor triangles of each vertex for applying bilateral filter, and computes its normal vector. Then it performs bilateral filtering to estimate new vertex position and to update its normal vector.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.871-877
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• 2005

The purpose of this paper is to identify the CPU temperatures on traffic processing between two servers system. To test this model, this research applies multi-generator and resource reservation protocol that produce various types of traffics. The empirical results indicate that $56^{\circ}C\mp9^{\circ}C$ of CPU temperature is suitable when 250-300 traffics with 10-15kb per a packet are supplied. And also, no jitter delay time is showed in these cases.

• 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.

• Annual Conference of KIPS
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• 2014.04a
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• pp.31-32
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• 2014

다중의 명령어를 동시에 수행할 수 있는 멀티코어 시스템의 특성으로 하나의 시스템 내에서 태스크를 수행하면서 외부 이벤트의 발생에 의한 인터럽트를 동시에 처리할 수 있다. 각 태스크가 처리되어야 하는 시간에 제약성을 갖는 실시간 시스템에서는 스케줄러에 의해 CPU 코어에서의 수행이 제어되어야한다. 본 논문에서는 최적이라고 알려진 Pfair 멀티코어 스케줄러의 각 코어별 유휴시간을 정량적으로 평가함으로써 인터럽트 처리의 지연시간을 분석한다.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.55-61
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• 2006

Graphics rendering pipeline (application, geometry, and rasterizer) is the core of real-time graphics which is the most important functionality for computer games. Usually this rendering process is completed by both the CPU and the GPU, and a bottleneck can be located either in the CPU or the GPU. This paper focuses on reducing the bottleneck between the CPU and the GPU. We are proposing a method for improving the performance of parallel processing for real-time graphics rendering by separating the CPU operations (usually performed using a thread) into two parts: pure CPU operations and operations related to the GPU, and let them operate in parallel. This allows for maximizing the parallelism in processing the communication between the CPU and the GPU. Some experiments lead us to confirm that our method proposed in this paper can allow for faster graphics rendering. In addition to our method of using a dedicated thread for GPU related operations, we are also proposing an algorithm for balancing the graphics pipeline using the idle time due to the bottleneck. We have implemented the two methods proposed in this paper in our networked 3D game engine and verified that our methods are effective in real systems.

• Korean Journal of Remote Sensing
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• v.5 no.1
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• pp.41-56
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• 1989

Improvement of the multichannel sea surface temperature(MCSST) software, which had been developed for the purpose of operating under mainframe computer system, was seeked in order to operate effectively in a mini computer system. CPU time and processing time, which is not a major factor under mainframe computer system, become a critical factor in real time image processing under mini computer system. Due to fixed kernel size(3$\times$4) of the old MCSST software, high spatial resolution characteristics of the original image received from satellites were apparently degraded when images are transformed into a cartesian coordinate system after geometrical distortions of the image due to earth curvature are removed. CPU and processing time were reduced to 0.13 and 0.15~0.22 comparing with the old MCSST's, respectively, by applying disk block I/O and M/T queue I/O method under VAX-11/750 computer. The high resolution quality (1.1km in AVHRR) of the processed image was guaranted using 2$\times$2 kernel size and applying moving window techniques without sacrificing CPU and processing time much.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.111-122
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• 2003

Military wargame simulation models must support the HLA in order to facilitate interoperability with other simulations, and using parallel simulation engines offer efficiency in reducing system overhead generated by propelling interoperability. However, legacy military simulation model engines process events using sequential event-driven method. This is due to problems generated by parallel processing such as synchronous reference to global data domains. Additionally. using legacy simulation platforms result in insufficient utilization of multiple CPUs even if a multiple CPU system is under use. Therefore, in this paper, we propose conversing the simulation engine to an object model-based parallel simulation engine to ensure military wargame model's improved system processing capability, synchronous reference to global data domains, external simulation time processing, and the sequence of parallel-processed events during a crash recovery. The converted parallel simulation engine is designed and implemented to enable parallel execution on a multiple CPU system (SMP).

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.1-10
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• 2014

This paper proposes a parallel implementation of the video based 4-stage fire detection algorithm using a general-purpose graphics processing unit (GPGPU) to support real-time processing of the high computational algorithm. In addition, this paper compares the performance of the GPGPU based fire detection implementation with that of the CPU implementation to show the effectiveness of the proposed method. Experimental results using five fire included videos with an SXGA ($1400{\times}1050$) resolution, the proposed GPGPU implementation achieves 6.6x better performance that the CPU implementation, showing 30.53ms per frame which satisfies real-time processing (30 frames per second, 30fps) of the fire detection algorithm.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.264-271
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• 2011

In this paper we examine and compare the computational speeds of three-dimensional (3D) object recognition by use of digital holography based on central unit processing (CPU) and graphic processing unit (GPU) computing. The holographic fringe pattern of a 3D object is obtained using an in-line interferometry setup. The Fourier matched filters are applied to the complex image reconstructed from the holographic fringe pattern using a GPU chip for real-time 3D object recognition. It is shown that the computational speed of the 3D object recognition using GPU computing is significantly faster than that of the CPU computing. To the best of our knowledge, this is the first report on comparisons of the calculation time of the 3D object recognition based on the digital holography with CPU vs GPU computing.