PDF-NVIDIA SURROUNDCONFIGURATIONS

2WAY SLI GTX 680 2D SURROU Graphics Processing nit GPSLI ModeConnector Diagrams Option 2 Option 3 2 monitors must be connected to the one GPU and 1 monitor to 2nd GPU The GPU with one monitor conn. Why NVIDIA GeForce Motherboard GPUs? NVIDIA NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Retain performance. CUDA C/C++. Based on industry-standard C/C++. Small set of extensions to enable heterogeneous programming. 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. What are Summed Area Tables (SATs)?•integrated texture representation•Invented by Frank Crow, 1984•Each texelis the sum of all texelsbelow and to the left of it•Allows rapid box fi Legend AMD specific NVIDIA specific Why Tessellate?Enables substantial geometric fidelityGPU side expansion very efficientScale performance and qualityProgrammatic LOD controlCompute at lower, adaptiv Ang. . Li. University . of . Wisconsin-Madison. Outline. Brief Introduction of Background. Implementation. Evaluation. Conclusion. 3/20/2013. 2. NVIDIA GTC 2013. Background. JPEG Encoding. Parallelism Seeking. OpenACC. Directives. subroutine . saxpy. (n, a, x, y) . real :: x(:), y(:), a. integer :: n, . i. $!. acc. kernels. do . i. =1,n.   y(. i. ) = a*x(. i. )+y(. i. ). . enddo. $!. acc. end kernels. GDC 2011 NVIDIA GDC 2011 NVIDIA SPONSORED SESSIONS Samuel Gateau, NVIDIA Dominic Filion , Blizzard Stereoscopic 3D Demystified : From Theory to Implementation in Starcraft 2 GDC 2011 NVIDIA SPONSORE 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. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Acknowledgements. CPU slides – Varun Sampath, NVIDIA. GPU . slides. Kayvon . Fatahalian. , CMU. Mike Houston, . NVIDIA. CPU and GPU Trends. II. GPGPUs. Instructor:Mikko. H . Lipasti. Spring 2015. University of Wisconsin-Madison. Lecture notes based on slides created by John . Shen. , Mark Hill, David Wood, . Guri. . Sohi. , Jim Smith, Natalie Enright . ECE 498AL, University of Illinois, Urbana-Champaign. 1. GPU Programming with CUDA . © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007-2009. ECE 498AL, University of Illinois, Urbana-Champaign. 2. A quiet revolution and potential build-up. Waters. Introduction to GPU Computing. Brief History of GPU Computing. Technical Issues. Social Impact. Marketing and Ethical . Issues. Project Management. Conclusion. Table of Contents. A . GPU is . 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