• Smart Media Journal
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• v.3 no.1
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• pp.33-38
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• 2014

Even though the performance of microprocessor is improved continuously, the performance improvement of computing system becomes hard to increase, in order to some drawbacks including increased power consumption. To solve the problem, general-purpose computing on graphics processing units(GPGPUs), which execute general-purpose applications by using specialized parallel-processing device representing graphics processing units(GPUs), have been focused. However, the characteristics of applications related with graphics is substantially different from the characteristics of general-purpose applications. Therefore, GPUs cannot exploit the outstanding computational resources sufficiently due to various constraints, when they execute general-purpose applications. When designing GPUs for GPGPU, memory system is important to effectively exploit the GPUs since typically general-purpose applications requires more memory accesses than graphics applications. Especially, external memory access requiring long latency impose a big overhead on the performance of GPUs. Therefore, the GPU performance must be improved if hierarchical memory architecture which can reduce the number of external memory access is applied. For this reason, we will investigate the analysis of GPU performance according to hierarchical cache architectures in executing various benchmarks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.201-206
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• 2018

As the artificial intelligence and big data technology has been developed recently, the importance of GPGPU, which is a general purpose graphics processing unit, is emphasized. In addition, by the demand for mining equipment to obtain bit coins, which is a block chain application technology, the price of GPGPU has increased sharply with scarcity. If a GPGPU can be precisely simulated, it is possible to conduct experiments on various GPGPU types and analyze performance without purchasing expensive ones. In this paper, we investigate the configuration of a GPGPU simulator and measure the performance of various benchmark programs using GPGPU-Sim.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.271-271
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• 2018

그래픽 처리 장치(GPU: Graphic Processing Units)는 그래픽 처리에 특화된 수많은 산술논리연산자 (ALU: Arithmetic Logic Unit)와 이에 관련된 인스트럭션Instruction)으로 인해 중앙 처리 장치(CPU: Central Processing Units) 보다 훨씬 빠른 계산 처리를 수행할 수 있다. 최근에는 FORTRAN에 의해 구현된 많은 수치모형들이 현실적인 모델링 방법의 발달로 인해 더 많은 계산량과 계산시간을 필요로 한다. 이 연구에서는 GPU 상의 범용 계산GPGPU : General-Purpose computing on Graphics Processing Units) 기반 운동파 강우유출모형(Kinematic Wave Rainfall-Runoff Model)이 CUDA(Compute Unified Device Architecture) FORTRAN을 사용하여 구현되었다. CUDA FORTRAN 운동파 강우유출모형의 계산 결과는 검증된 CPU 기반 운동파 강우유출모형의 계산 결과와 비교하여 검증되었으며, 잘 일치함을 보여 주었다. CUDA FORTRAN 운동파 강우유출모형은 CPU 기반 모형에 비해 약 20 배 더 빠른 계산 시간을 보였다. 또한 계산 영역이 커짐에 따라 CPU 버전에 비해 CUDA FORTRAN 버전의 계산 효율이 향상되었다.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.287-287
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• 2021

본 연구에서는 CUDA(Compute Unified Device Architecture) 포트란을 이용하여 확산파 강우 유출모형을 개발하였다. CUDA 포트란은 그래픽 처리 장치(Graphic Processing Unit: GPU)에서 수행하는 병렬 연산 알고리즘을 포트란 언어를 사용하여 작성할 수 있도록 하는 GPU상의 범용계산(General-Purpose Computing on Graphics Processing Units: GPGPU) 기술이다. GPU는 그래픽 처리 작업에 특화된 다수의 산술 논리 장치(Arithmetic Logic Unit: ALU)로 구성되어 있어서 중앙 처리 장치(Central Processing Unit: CPU)보다 한 번에 더 많은 연산 수행이 가능하다. 이에 따라, CUDA 포트란기반 확산파모형은 분포형 강우유출모형의 수치모의 연산시간을 단축시킬 수 있다. 분포형모형의 지배방정식은 확산파모형과 Green-Ampt모형으로 구성되었고, 확산파모형은 유한체적법을 이용하여 이산화 하였다. CUDA 포트란기반 확산파모형의 정확성은 기존 연구된 수리실험 결과 및 CPU기반 강우유출모형과 비교하였으며, 연산소요시간에 대한 효율성은 CPU기반 확산파모형과 비교하였다. 그 결과 CUDA 포트란기반 확산파모형의 결과는 수리실험 결과 및 CPU기반 강우유출모형의 결과와 유사한 결과를 나타냈다. 또한, 연산소요시간은 CPU 기반 확산파모형의 연산소요시간보다 단축되었으며, 본 연구에 사용된 장비를 기준으로 최대 100배 정도 단축되었다.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.275-284
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• 2010

GPUs are stream processors based on multi-cores, which can process large data with a high speed and a large memory bandwidth. Furthermore, GPUs are less expensive than multi-core CPUs. Recently, usage of GPUs in general purpose computing has been wide spread. The CUDA architecture from Nvidia is one of efforts to help developers use GPUs in their application domains. In this paper, we propose techniques to parallelize a skyline algorithm which uses a simple nested loop structure. In order to employ the CUDA programming model, we apply our optimization techniques to make our skyline algorithm fit into the performance restrictions of the CUDA architecture. According to our experimental results, we improve the original skyline algorithm by 80% with our optimization techniques.

• Journal of IKEEE
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• v.13 no.2
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• pp.253-259
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• 2009

In this paper, we propose a novel architecture of a general graphics shader processor without a dedicated hardware. Recently, mobile devices require the high performance graphics processor as well as the small size, low power. The proposed shader processor is a GP-GPU(General-Purpose computing on Graphics Processing Units) to execute the whole OpenGL ES 2.0 graphics pipeline by using shader instructions. It does not require the separate dedicate H/W such as rasterization on this fully programmable capability. The fully programmable 3D graphics shader processor can reduce much of the graphics hardware. The chip size of the designed shader processor is reduced 60% less than the sizes of previous processors.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.9-21
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• 2013

Due to increased computing power and flexibility of GPU, recent GPUs execute general purpose parallel applications as well as graphics applications. Programmers can use GPGPU by using the APIs from GPU vendors. Unfortunately, computational resources of GPU are not fully utilized when executing general purpose applications because of frequent branch instructions. To handle the branch problem, several warp formations have been proposed. Intuitively, we expect that the warp formations providing higher computational resource utilization show higher performance. Contrary to our expectations, according to simulation results, the performance of the warp formation providing better utilization is lower than that of the warp formation providing worse utilization. This is because warp formation providing high utilization causes serious memory bottleneck due to increased memory request. Therefore, warp formation providing high computation utilization cannot guarantee high performance without proper hardware resources. For this reason, we will analyze the correlation between hardware configuration and warp formation. Our simulation results present the guideline to solve the underutilization problem due to branch instructions when designing recent GPU.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.1-11
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• 2015

In recent high performance computing system, GPGPU has been widely used to process general-purpose applications as well as graphics applications, since GPU can provide optimized computational resources for massive parallel processing. Unfortunately, GPGPU doesn't exploit computational resources on GPU in executing general-purpose applications fully, because the applications cannot be optimized to GPU architecture. Therefore, we provide GPU research guideline to improve the performance of computing systems using GPGPU. To accomplish this, we analyze the negative factors on GPU performance. In this paper, in order to clearly classify the cause of the negative factors on GPU performance, GPU core status are defined into 5 status: fully active status, partial active status, idle status, memory stall status and GPU core stall status. All status except fully active status cause performance degradation. We evaluate the ratio of each GPU core status depending on the characteristics of benchmarks to find specific reasons which degrade the performance of GPU. According to our simulation results, partial active status, idle status, memory stall status and GPU core stall status are induced by computational resource underutilization problem, low parallelism, high memory requests, and structural hazard, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1119-1128
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• 2013

In this paper, we propose an algorithm to detect and track the human face with a GPU-based high speed. Basically the detection algorithm uses the existing Adaboost algorithm but the search area is dramatically reduced by detecting movement and skin color region. Differently from detection process, tracking algorithm uses only depth information. Basically it uses a template matching method such that it searches a matched block to the template. Also, In order to fast track the face, it was computed in parallel using GPU about the template matching. Experimental results show that the GPU speed when compared with the CPU has been increased to up to 49 times.