• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.77-84
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• 2023

The utilization of GPUs on general-purpose computers is currently on the rise due to the increase in its programmability and performance requirements. The utility of tools like NVIDIA's CUDA have been designed to allow programmers to code algorithms by using C-like language for the execution process on the graphics processing units GPU. Unfortunately, many of the performance and correctness bugs will happen on parallel programs. The CUDA tool support for the parallel programs has not yet been actualized. The use of a dynamic analyzer to find performance and correctness bugs in CUDA programs facilitates the execution of sophisticated processes, especially in modern computing requirements. Any race conditions bug it will impact of program correctness and the share memory bank conflicts to improve the overall performance. The technique instruments the programs in a way that promotes accessibility of the memory locations accessed by different threads well as to check for any bugs in the code of a program. The instrumented source code will be used initiated directly in the device emulation code of CUDA to send report for the user about all errors. The current degree of automation helps programmers solve subtle bugs in highly complex programs or programs that cannot be analyzed manually.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.160-160
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• 2017

자동화 기술을 통한 한국형 스마트팜의 발전이 비약적으로 이루어지고 있는 가운데 무인화를 위한 지능적인 스마트 시설환경 관찰 및 분석에 대한 요구가 점점 증가 하고 있다. 스마트 시설환경에서 취득 가능한 시계열 데이터는 온도, 습도, 조도, CO2, 토양 수분, 환기량 등 다양하다. 시스템의 경계가 명확함에도 해당 속성의 특성상 타임도메인과 공간도메인 상에서 정확한 추정 또는 예측이 난해하다. 시설 환경에 접목이 증가하고 있는 지능형 관리 기술 구현을 위해선 시계열 공간 데이터에 대한 신속하고 정확한 정량화 기술이 필수적이라 할 수 있다. 이러한 기술적인 요구사항을 해결하고자 시도되는 다양한 방법 중에서 공간 분해능 향상을 위한 다지점 계측 메트릭스를 실험적으로 구성하였다. $50m{\times}100m$의 단면적인 연동 딸기 온실을 대상으로 $3{\times}3{\times}3$의 3차원 환경 인자 계측 매트릭스를 설치하였다. 1 Hz의 주기로 4가지 환경인자(온도, 습도, 조도, CO2)를 계측하였으며, 계측 하는 시점과 동시에 병렬적으로 공간통계법을 이용하여 미지의 지점에 대한 환경 인자들을 실시간으로 추정하였다. 선행적으로 50 cm 공간 분해능에 대응하기 위하여 Kriging interpolation법을 횡단면에 대하여 분석한 후 다시 종단면에 대하여 분석하였다. 3 Ghz에 해당하는 연산 능력을 보유한 컴퓨터에서 1초 동안 획득한 데이터에 대한 분석을 마치는데 소요되는 시간이 15초 내외로 나타났다. 이는 해당 알고리즘의 매우 높은 시간 복잡도(Order of $O=O^3$)에 기인하는 것으로 다양한 시설 환경의 관리 방법론에 적절히 대응하기에 한계가 있다 할 수 있다. 실시간으로 시간 복잡도가 높은 연산을 수행하기 위한 기술적인 과제를 해결하고자, 근래에 관심이 증가하고 있는 NVIDIA 사에서 제공하는 CUDA 엔진과 Apple사의 제안을 시작으로 하여 공개 소프트웨어 개발 컨소시엄인 크로노스 그룹에서 제공하는 OpenCL 엔진을 비교 분석하였다. CUDA 엔진은 GPU(Graphics Processing Unit)에서 정보 분석 프로그램의 연산 집약적인 부분만을 담당하여 신속한 결과를 산출할 수 있는 라이브러리이며 해당 하드웨어를 구비하였을 때 사용이 가능하다. 반면, OpenCL은 CUDA 엔진이 특정 하드웨어에서 구동이 되는 한계를 극복하고자 하드웨어에 비의존적인 라이브러리를 제공하는 것이 다르며 클러스터링 기술과 연계를 통해 낮은 하드웨어 성능으로 인한 단점을 극복하고자 하였다. 본 연구에서는 CUDA 8.0(https://developer.nvidia.com/cuda-downloads)버전과 Pascal Titan X(NVIDIA, CA, USA)를 사용한 방법과 OpenCL 1.2(https://www.khronos.org/opencl/)버전과 Samsung Exynos5422 칩을 장착한 ODROID-XU4(Hardkernel, AnYang, Korea)를 사용한 방법을 비교 분석하였다. 50 cm의 공간 분해능에 대응하기 위한 4차원 행렬($100{\times}200{\times}5{\times}4$)에 대하여 정수 지수화를 위한 Quantization을 거쳐 CUDA 엔진과 OpenCL 엔진을 적용한 비교한 결과, CUDA 엔진은 1초 내외, OpenCL 엔진의 경우 5초 내외의 연산 속도를 보였다. CUDA 엔진의 경우 비용측면에서 약 10배, 전력 소모 측면에서 20배 이상 소요되었다. 따라서 우선적으로 OpenCL 엔진 기반 하드웨어 가속 기술 최적화 연구를 통해 스마트 시설환경 실시간 시뮬레이션 기술 도입을 위한 기술적 과제를 풀어갈 것이다.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.271-277
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• 2017

As a result of rapid industrialization, air pollutants are seriously threatening the health of the people, the forecast is becoming more and more important. In forecasting air quality, it is very important to create a reliable initial field because the initial field input to the air quality forecasting model affects the accuracy of the forecast. There are several methods for enhancing the initial field input. One of the necessary techniques is data assimilation. The number of operations and the time required for such data assimilation is exponentially increased as the forecasting area is widened and the number of observation sites increases. Therefore, as the forecast size increases, it is difficult to apply the existing sequential processing method to a field requiring fast processing speed. In this paper, we propose a method that can process Cresman's method, which is one of the data assimilation techniques, in real time using CUDA. As a result, the proposed parallel processing method using CUDA improved at least 35 times faster than the conventional sequential method and other parallel processing methods.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.169-176
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• 2012

This paper presents a parallel object-oriented programming (OOP) platform for efficient visualization of three-dimensional content in CUDA environments. For this purpose, this paper discusses the features and limitations in implementing C++ object-oriented codes using CUDA and proposes the solutions. Also, it presents how to implement a 3D parallel visualization platform based on the MVC (Model/View/Controller) design pattern. Also, it provides sample implementations for integral MLS (iMLS) and signed distance fields (SDFs) based on the Marching Cubes and Raytracing. The proposed approach enables GPU parallel processing only by implementing simple interfaces. Based on this, developers can expect general benefits that are common in general OOP techniques including abstractization and inheritance. Though I implemented only two specific samples in this paper, I expect my approach can be widely applied to general computer graphics problems.

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

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.31 no.4
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• pp.42-50
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• 2013

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.441-448
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• 2008

The molecular docking system needs a large amount of computation and requires super-computing power. Since the experiment requires a large amount of time, the experiment is conducted in the distributed environment or in the grid environment. Recently, researches on using parallel GPU of far higher performance than that of CPU in scientific computing have been very actively conducted. CUDA is an open technique by which a parallel GPU programming is made possible. This study proposes the molecular docking system using CUDA. It also proposes algorithm that parallels energy-minimizing-computation. To verify such experiments, this study conducted a comparative analysis on the time required for experimenting molecular docking in general CPU and the time and performance of the parallel GPU-based molecular docking which is proposed in this study.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.243-248
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• 2012

In the information retrieval systems like vector model implementation and document clustering, document similarity calculation takes a great part on the overall performance of the system. In this paper, GPU parallelism has been explored to enhance the processing speed of document similarity calculation in a CUDA framework. The proposed method increased the similarity calculation speed almost 15 times better compared to the typical CPU-based framework. It is 5.2 and 3.4 times better than the methods by using CUBLAS and Thrust, respectively.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.156-164
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• 2017

Measuring the similarity of two graphs is a basic tool to solve graph problems in various applications. Most graph algorithms have a high time complexity according to the number of vertices and edges. Because Graphics Processing Units (GPUs) have a high computational power and can be obtained at a low cost, these have been widely used in graph applications to improve execution time. This paper proposes an efficient parallel algorithm to measure graph similarity using the CUDA on a GPU environment. The experimental results show that the proposed approach brings a considerable improvement in performance and efficiency when compared to CPU-based results. Our results also show that the performance is improved significantly as the size of the graph increases.