• KIISE Transactions on Computing Practices
• /
• v.20 no.9
• /
• pp.507-512
• /
• 2014

Recently, Distributed computing processing begins using both CPU(Central processing unit) and GPU(Graphic processing unit) to improve the performance to overcome darksilicon problem which cannot use all of the transistors because of the electric power limitation. There is an integrated graphics processor that CPU and GPU share memory and Last level cache(LLC). But, There is no LLC access rules between CPU and GPU, so if GPU and CPU processes run together at the same time, performance of both processes gets worse because of the contention on the LLC. This Paper gives evidence to prove the need of the Cache Partitioning and is mentioned about the cache partitioning design using page coloring to allocate the L3 Cache space only for the GPU process to guarantee GPU process performance.

• Proceedings of the KOSOMBE Conference
• /
• v.1994 no.05
• /
• pp.49-52
• /
• 1994

We have developed a biological signal measurement, archiving, and communication system (SiMACS). The front end of the system is the intelligent data processing unit (IDPU) which includes ECG, EEG, EMG, blood pressure, respiration, temperature measurement modules, module control and data acquisition unit, real-time display and signal processing unit. IDPUS are connected to central data base unit through LAN(Ethernet). Workstations which receive signals from central DB and provide various signal analysis tools are also connected to the network. The developed PC-based SiMACS is described.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.323-323
• /
• 2020

그래픽 처리 장치(Graphic Processing Unit: GPU)는 그래픽 처리 작업에 특화된 다수의 산술논리 장치(Arithmetic Logic Unit: ALU)로 구성되어 있어서 중앙 처리 장치(Central Processing Unit: CPU)보다 한 번에 더 많은 연산 수행이 가능하다. 본 연구는 GPU 가속 운동파모형을 실제 유역에 적용하여, GPU 가속 운동파 강우유출모형 결과에 대한 정확성과 연산 소요 시간에 대한 효율성을 확인하였다. GPU 가속 운동파모형은 분포형 강우유출모형의 수치모의 연산시간을 단축시키기 위해 CUDA 포트란을 이용하여 개발되었다. 분포형모형의 지배방정식은 운동파모형과 Green-Ampt모형으로 구성되었고, 운동파모형은 유한체적법을 이용하여 이산화 하였다. GPU 가속 운동파모형을 이용하여 금강의 미호천 유역에서 발생하는 강우유출현상을 모의 하였고, 동일한 유한체적법을 이용한 CPU(Central Processing Unit) 기반의 강우유출모형과 비교하였다. 그 결과 GPU 가속모형의 결과는 미호천 유역 하류단에서 관측한 결과와 유사한 결과를 나타냈다. 또한, 연산소요시간은 CPU 기반의 강우유출모형의 연산소요시간보다 단축되었으며, 본 연구에 사용된 장비를 기준으로 최대 100배 정도 단축되었다.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.5 no.1
• /
• pp.85-105
• /
• 1981

Information is an essential factor leading the rapid progress which is one of the distinguished characteristics in modem society. As more information is required and as more is supplied by individuals, governmental units, businesses, and educational institutions, the greater will be the requirement for efficient methods of communication. One possibility for improving the information dissemination process is to use computers. The capabilities of such machine are beginning to be used in the process of Information storage, retrieval and dissemination. An important problems, that must be carefully examined is whether one technique for information retrieval is better for worse than another. This paper examines problem of how to evaluate an information retrieval system. One specific approach is a cost accounting model for use in studying how to minimize the cost of operating a mechanized retrieval system. Through the use of cost analysis, the model provides a method for comparative evaluation between systems. The general cost accounting model of the literature retrieval system being designed by this study are given below. 1. The total cost accounting model of the literature retrieval system. The total cost of the literature retrieval system = (the cost per unit of user time X the amount of user time) + ( the cost per unit of system time X the amount of system time) 2. System cost accounting model system cost = (the pre-search system cost per unit of time X time) + (the search system cost per unit of time X time) + (the post search system cost per unit of time X time) 1) Pre-search system cost per unit of time = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 2) Search system cost per unit of time = comparison cost + document representation cost. 3) Post-search system cost per unit of time. = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 3. User cost accounting model Total user cost = [pre-search user cost per unit of time X (time + additional time) ] + [search user cost per unit of time X (time + additional time) ] + [post-search user cost per unit of time X (time + additional time) ].

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.201_202
• /
• 2009

This paper presents a design methodology of a distribution transformer with condition monitoring sensors and its data processing unit. The proposed distribution transformer includes various type of condition monitoring sensors such as load current/voltage, temperature and heat aging of insulating oil. The data processing unit is installed at the distribution transformer site. It integrates sensed data and transmits these to a central server system. The proposed distribution transformer and its data processing unit will help an on-line condition monitoring system for distribution transformers.

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

In this paper, we propose a CUDA(Common Unified Device Architecture) based SW(software) design method for CPU(Central Processing Unit) and GPU(Graphic Processing Unit) parallel structure to implement real-time process in 3D Laser ladar(LADAR) imaging system. LADAR is a complex system to generate 3-dimensional image based on the laser ranging information, and requires massive process resources in each phase. Therefore, designing and implementing parallel structure are crucial to realize a real-time process within limited system resource. As a conclusion, we can meet the speed of required real-time process allocating separable work load to CUDA GPU by analyzing process algorithm in each phase and confirm the process speed increase by 46%.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.135-138
• /
• 2013

GPGPU(General Purpose Graphics Processing Unit) 병렬처리 시스템인 CUDA(Compute Unified Device Architecture)는 컴퓨터에서의 고속 연산 처리를 위해 많이 사용되어왔다. CUDA에서 연산 처리를 하기 위해서는 CUDA의 특성을 이해해야 한다. CUDA는 CPU(Central Processing Unit)가 처리하는 Host 영역과 GPU(Graphics Processing Unit)가 처리하는 영역인 Device 영역이 존재하며, 이 두 영역간의 데이터 복사를 통해 연산 처리를 진행한다. 이런 구조적인 특성상 메인 메모리에서 GPU 메모리로 입력 데이터를 전달해야 GPU를 이용해 연산을 처리할 수 있는 구조를 가지고 있다. 하지만 이러한 처리 구조로 인해 연산 시간과 별도로 메인 메모리와 GPU 메모리간의 데이터 복사시간이 존재하며, 추가적으로 발생하는 메모리 복사 시간으로 인해 오버헤드가 발생하게 된다. 본 논문에서는 실험을 통해 메모리 복사 시간, 연산의 반복 횟수 그리고 연산의 복잡성이 전체 성능에 어떤 영향을 미치는지 논하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4948-4967
• /
• 2017

On mobile devices, image sequences are widely used for multimedia applications such as computer vision, video enhancement, and augmented reality. However, the real-time processing of mobile devices is still a challenge because of constraints and demands for higher resolution images. Recently, heterogeneous computing methods that utilize both a central processing unit (CPU) and a graphics processing unit (GPU) have been researched to accelerate the image sequence processing. This paper deals with various optimizing techniques such as parallel processing by the CPU and GPU, distributed processing on the CPU, frame buffer object, and double buffering for parallel and/or distributed tasks. Using the optimizing techniques both individually and combined, several heterogeneous computing structures were implemented and their effectiveness were analyzed. The experimental results show that the heterogeneous computing facilitates executions up to 3.5 times faster than CPU-only processing.

• Journal of Korea Water Resources Association
• /
• v.52 no.11
• /
• pp.887-894
• /
• 2019

We proposed a GPU (Grapic Processing Unit) accelerated kinematic wave model for rainfall runoff simulation and tested the accuracy and speed up performance of the proposed model. The governing equations are the kinematic wave equation for surface flow and the Green-Ampt model for infiltration. The kinematic wave equations were discretized using a finite volume method and CUDA fortran was used to implement the rainfall runoff model. Several numerical tests were conducted. The computed results of the GPU accelerated kinematic wave model were compared with several measured and other numerical results and reasonable agreements were observed from the comparisons. The speed up performance of the GPU accelerated model increased as the number of grids increased, achieving a maximum speed up of approximately 450 times compared to a CPU (Central Processing Unit) version, at least for the tested computing resources.

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