PPT-© NVIDIA 2013 Introduction to CUDA

heterogeneous programming Brian Gregor bgregorbuedu Research Computing Services Boston University CUDA CC BASICS NVIDIA Corporation NVIDIA 2013 What is CUDA CUDA Architecture Expose GPU parallelism for generalpurpose computing. Basically a child CUDA Kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel The parent CUDA kernel can consume the output produced from the child CUDA Kernel all withou t 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. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. GPGPU Programming in CUDA. Supada . Laosooksathit. NVIDIA Hardware Architecture. Host. memory. Recall. 5 steps for CUDA Programming. Initialize device. Allocate. device memory. Copy. data to device memory. 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. Proposed Work. This . work aims . to enable efficient dynamic memory management on NVIDIA GPUs by utilizing a sub-allocator between CUDA and the programmer. This work enables Many-Task Computing applications, which need to dynamically allocate parameters for each task, to run efficiently on GPUs.. 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). on . Ubuntu. Cuda. download site. . https://developer.nvidia.com/cuda-downloads. $ . sudo. . dpkg. -. i. cuda-repo-ubuntu1404_7.5-18_amd64.deb . $ . sudo. apt-get update . $ . sudo. apt-get install . 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 . of. Split. Performance. . comparison. for NVIDIA CUDA . and. Intel . Xeon. . Phi. May, 2016. Contents. . Introduction. NVIDIA CUDA. Intel . Xeon. . Phi. . Conclusion. . tCSC. 2016. . t. oday’s. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. 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: .