PPT-Canny Edge Detection Using an NVIDIA GPU and CUDA

Alex Wade CAP6938 Final Project Introduction GPU based implementation of A Computational Approach to Edge Detection by John Canny Paper presents an accurate localized edge detection method Purpose. Author: Michael Sedivy. Introduction. Edge Detection in Image Processing. MCMC and the Use of Gibbs Sampler. Input. Results. Conclusion/Future Work. References. Edge Detection. Detecting Edges in images is a complex task, but it useful in other image processing problems. ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. Håkon Kvale . Stensland. iAD-lab, Department for Informatics. Basic 3D Graphics Pipeline. Application. Scene Management. Geometry. Rasterization. Pixel Processing. ROP/FBI/Display. Frame. Buffer. Memory. Supercomputing. The Next wave of HPC. Presented by Shel Waggener. HP Materials from Marc Hamilton. June. , . 2011. © Copyright 2011 Hewlett-Packard Development Company, L.P.    . GPUs – changing the Economics of Supercomputing. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Intel. ®. Core i5 and i7 “. Calpella. ” Based Platforms . October, 2010. NVIDIA Quadro. Range of Professional Graphics Solutions. Images courtesy of Softimage Co. and Avid Technology Inc., model provided by Acony Games.. Vinay B Gavirangaswamy. Canny edge detection algorithm. Output. Original. Single Threaded. Output (contd.). Original . Multi-Threaded (. OpenMP. ). Output (contd.). Original. Multi-Threaded. (GPU-CUDA). heterogeneous programming. Brian Gregor. bgregor@bu.edu. Research Computing Services. Boston University. CUDA C/C BASICS. NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. K. ainz. Overview. About myself. Motivation. GPU hardware and system architecture. GPU programming languages. GPU programming paradigms. Pitfalls and best practice. Reduction and tiling examples. State-of-the-art . Research Computing Services. Boston . University. GPU Programming. Access to the SCC. Login: . tuta#. Password: . VizTut#. GPU Programming. Access to the SCC GPU nodes. # copy tutorial materials: . Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. CUDA NEW FEATURES AND UPDATES 2 ANNOUNCING CUDA 10.2 Download today at: https://developer.nvidia.com/cuda - downloads Plus Compiler, Tools and Library Enhancements & Performance Improvements See Relea . Edge Detection. Consider this picture. We would like its output to be. So, to repeat: The purpose of Edge Detection is to find jumps in the brightness function (of an image) and mark them. . Before we get into details, we need to detour.