• Title, Summary, Keyword: CUDA

Search Result 250, Processing Time 0.032 seconds

A Road Region Extraction Using OpenCV CUDA To Advance The Processing Speed (처리 속도 향상을 위해 OpenCV CUDA를 활용한 도로 영역 검출)

  • Lee, Tae-Hee;Hwang, Bo-Hyun;Yun, Jong-Ho;Choi, Myung-Ryul
    • Journal of Digital Convergence
    • /
    • v.12 no.6
    • /
    • pp.231-236
    • /
    • 2014
  • In this paper, we propose a processing speed improvement by adding a parallel processing based on device(graphic card) into a road region extraction by host(PC) based serial processing. The OpenCV CUDA supports the many functions of parallel processing method by interworking a conventional OpenCV with CUDA. Also, when interworking the OpenCV and CUDA, OpenCV functions completed a configuration are optimized the User's device(Graphic Card) specifications. Thus, OpenCV CUDA usage provides an algorithm verification and easiness of simulation result deduction. The proposed method is verified that the proposed method has a about 3.09 times faster processing speed than a conventional method by using OpenCV CUDA and graphic card of NVIDIA GeForce GTX 560 Ti model through experimentation.

A Simulation Framework for CUDA Computing on Non-x86 Platforms based on QEMU and GPGPU-Sim (비x86 플랫폼 상에서의 CUDA 컴퓨팅을 위한 QEMU 및 GPGPU-Sim 기반 시뮬레이션 프레임워크 개발)

  • Hwang, Jaemin;Choi, Jong-Wook;Choi, Seongrim;Nam, Byeong-Gyu
    • Journal of the Korea Industrial Information Systems Research
    • /
    • v.19 no.2
    • /
    • pp.15-22
    • /
    • 2014
  • This paper proposes a CUDA simulation framework for non-x86 computing platforms based on QEMU and GPGPU-sim. Previous simulators for heterogeneous computing platforms did not support for non-x86 CPU models or CUDA computing platform. In this work, we combined the QEMU and the GPGPU-Sim to support the non-x86 CPU models and the CUDA platform, respectively. This approach provides a simulation framework for CUDA computing on non-x86 CPU models.

Implementation of Particle Swarm Optimization Method Using CUDA (CUDA를 이용한 Particle Swarm Optimization 구현)

  • Kim, Jo-Hwan;Kim, Eun-Su;Kim, Jong-Wook
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.58 no.5
    • /
    • pp.1019-1024
    • /
    • 2009
  • In this paper, particle swarm optimization(PSO) is newly implemented by CUDA(Compute Unified Device Architecture) and is applied to function optimization with several benchmark functions. CUDA is not CPU but GPU(Graphic Processing Unit) that resolves complex computing problems using parallel processing capacities. In addition, CUDA helps one to develop GPU softwares conveniently. Compared with the optimization result of PSO executed on a general CPU, CUDA saves about 38% of PSO running time as average, which implies that CUDA is a promising frame for real-time optimization and control.

Analysis of Programming Techniques for Creating Optimized CUDA Software (최적화된 CUDA 소프트웨어 제작을 위한 프로그래밍 기법 분석)

  • Kim, Sung-Soo;Kim, Dong-Heon;Woo, Sang-Kyu;Ihm, In-Sung
    • Journal of KIISE:Computing Practices and Letters
    • /
    • v.16 no.7
    • /
    • pp.775-787
    • /
    • 2010
  • Unlike general-purpose CPUs, the GPUs have been specialized as many-core streaming processors, and are frequently replacing the CPUs in an increasing range of computations thanks to their outstanding parallel computing capacity. In order to respond to such trend, NVIDIA has recently issued a new parallel computing architecture called CUDA(Compute Unified Device Architecture), offering a flexible GPU programming environment for GPGPU(General Purpose GPU) computing. In general, when programmers use the CUDA API, they should clearly understand many aspects of GPU's computing architecture to produce efficient parallel software. In this article, we explain several optimization techniques for CUDA programming that we have verified through a lot of experiment and trial and error, and review how those techniques affect the performance of code execution. In particular, we use a specific problem as an example to analyze several elements that affect performances, such as effective accesses to hierarchical memory system, processor occupancy, and latency hiding. In conclusion, we present several directions that may be utilized effectively in CUDA-based parallel programming.

Kinematic Wave Rainfall-Runoff Model Using CUDA FORTRAN (CUDA FORTRAN을 이용한 운동파 강우유출모형)

  • Kim, Boram;Kim, Dae-Hong
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • /
    • pp.271-271
    • /
    • 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.

  • PDF

An Image Processing Speed Enhancement in a Multi-Frame Super Resolution Algorithm by a CUDA Method (CUDA를 이용한 초해상도 기법의 영상처리 속도개선 방법)

  • Kim, Mi-Jeong
    • Journal of the Korea Institute of Military Science and Technology
    • /
    • v.14 no.4
    • /
    • pp.663-668
    • /
    • 2011
  • Although multi-frame super resolution algorithm has many merits but it demands too much calculation time. Researches have shown that image processing time can be reduced using a CUDA(Compute unified device architecture) which is one of GPGPU(General purpose computing on graphics processing unit) models. In this paper, we show that the processing time of multi-frame super resolution algorithm can be reduced by employing the CUDA. It was applied not to the whole parts but to the largest time consuming parts of the program. The simulation result shows that using a CUDA can reduce an operation time dramatically. Therefore it can be possible that multi-frame super resolution algorithm is implemented in real time by using libraries of image processing algorithms which are made by a CUDA.

CUDA based Lossless Asynchronous Compression of Ultra High Definition Game Scenes using DPCM-GR (DPCM-GR 방식을 이용한 CUDA 기반 초고해상도 게임 영상 무손실 비동기 압축)

  • Kim, Youngsik
    • Journal of Korea Game Society
    • /
    • v.14 no.6
    • /
    • pp.59-68
    • /
    • 2014
  • Memory bandwidth requirements of UHD (Ultra High Definition $4096{\times}2160$) game scenes have been much more increasing. This paper presents a lossless DPCM-GR based compression algorithm using CUDA for solving the memory bandwidth problem without sacrificing image quality, which is modified from DDPCM-GR [4] to support bit parallel pipelining. The memory bandwidth efficiency increases because of using the shared memory of CUDA. Various asynchronous transfer configurations which can overlap the kernel execution and data transfer between host and CUDA are implemented with the page-locked host memory. Experimental results show that the maximum 31.3 speedup is obtained according to CPU time. The maximum 30.3% decreases in the computation time among various configurations.

Real-Time Free Viewpoint TV System Using CUDA (CUDA 를 이용한 실시간 Free Viewpoint TV System 구현)

  • Yang, Yun Mo;Lee, Jin Hyeok;Oh, Byung Tae
    • Proceedings of the Korean Society of Broadcast Engineers Conference
    • /
    • /
    • pp.71-73
    • /
    • 2015
  • In this paper, we propose the Real-Time Free Viewpoint TV System with multiple Microsoft Kinects and CUDA of NVidia GPGPU library. It generates a virtual view between two views by using color and depth image acquired by Kinect in real time. In order to reduce complexity of coordinate transformations and nearest neighbor method for hole filling caused by IR pattern interference, we parallelize this process using CUDA. Finally, it is observed that CUDA based system generates more frames than using CPU based system in the same time.

  • PDF

A Study on a Declines in Performance by Memory Copy in CUDA (CUDA의 메모리 복사로 인한 성능 저하 연구)

  • Kang, Jihun;Lee, DaeWon;Kang, InSung;Yu, HeonChang
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • /
    • 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.

  • PDF

Improving 3D Measurement Speed using CUDA (CUDA를 이용한 3D 측정 속도 향상)

  • Kim, Ho-Joong;Cho, Tai-Hoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.331-334
    • /
    • 2017
  • Recently, a method using a fringe pattern is widely used for 3D measurements. This is a method of measuring by using a phase value obtained by projecting a pattern to an object to be measured. This method requires many operations such as calculating the phase value and calculating the height. It takes a lot of time depending on the amount of computation. In this paper, we present a method using NVIDIA's CUDA to reduce this time. And we introduce the method of calculating phase value and height. It also shows the exact time difference between the CPU version and the CUDA version. This method is very effective because it can process the same operation in a shorter time.

  • PDF