Performance Analysis of Sobel Edge Detection Filter on GPU using CUDA & OpenGL

Khyati Shah
Computer Engineering Department, VIER-kotambi, INDIA
International Journal for Research in Applied Science and Engineering Technology (IJRASET), Vol. 1, Issue III, 2013

   title={Performance Analysis of Sobel Edge Detection Filter on GPU using CUDA & OpenGL},

   author={Shah, Ms Khyati},



Download Download (PDF)   View View   Source Source   



CUDA(Compute Unified Device Architecture) is a novel technology of general-purpose computing on the GPU, which makes users develop general GPU (Graphics Processing Unit) programs easily. GPUs are emerging as platform of choice for Parallel High Performance Computing. GPUs are good at data intensive parallel processing with availability of software development platforms such as CUDA (developed by Nvidia for its Geforce series GPUs). Basic goal of CUDA is to help programmers focus on the task of parallelization of the algorithms rather than spending time on their implementation. It supports the Heterogeneous computation where applications use both the CPU and GPU. In this paper we propose the implementation of sobel edge detection filter on GeForce GT 130 on MAC OS using CUDA and OpenGL. We reduces the Global Memory using kernel function. Also compare their results and performance to the previous implementation and it gives the more optimized results.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1666 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

339 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: