PDF-NVIDIA GeForce GTX GPUDatasheet

NVIDIA GeForce GTX GPUDatasheet3D GraphicsFull Microsoft. 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. CUDA Platform. CUDA Parallel Computing Platform. . Hardware . . . Capabilities. GPUDirect. SMX. Dynamic Parallelism. HyperQ. Programming . Approaches. Libraries. “Drop-in” Acceleration. 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 Legend AMD specific NVIDIA specific Why Tessellate?Enables substantial geometric fidelityGPU side expansion very efficientScale performance and qualityProgrammatic LOD controlCompute at lower, adaptiv 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. 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 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 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. 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 C/C++ Basics Supercomputing 2011 Tutorial Cyril Zeller, NVIDIA Corporation Brian C. Radlinski, MD. April 4, 2014. Patient Scenarios. ___ _____ _______ __ _ ________ _______ ____ _ _______ . __ __ _____ _____ ___________ ___ . __. _ . _ . . __ _____ _______ ________ ___ _ .