PPT-Some GPU activities at the

CMS experiment Felice Pantaleo EPCMGCO 1 Outline Physics and Technologic Motivations Tracking HGCAL clustering CUDA Translation Conclusion 2 Physics and Technologic Motivations 3 Physics Motivation. . 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. Chris Rossbach, Microsoft Research. Jon Currey, Microsoft Research. Emmett . Witchel. , University of Texas at Austin. HotOS. 2011. Lots of GPUs. Must they be so hard to use?. We need dataflow…. GPU Haiku . Alan . Gray. EPCC . The University of Edinburgh. Outline. Why do we want/need accelerators such as GPUs?. Architectural reasons for accelerator performance advantages . Latest accelerator Products. NVIDIA and AMD GPUs. A CUDA Approach. Gary . Resnick. Scott . Badenhorst. Department of Computer Science. University of Cape Town. 17 March, 2010. Introduction. Approach. Plan. Outcomes. Overview. Radio Astronomy. By . Ishtiaq. . Hossain. Venkata. Krishna . Nimmagadda. Application of Jacobi Iteration. Cardiac Tissue is considered as a grid of cells.. Each GPU thread takes care of voltage calculation at one cell. This calculation requires Voltage values of neighboring cells. ITK v4 . . summer . meeting. June 28, 2011. Won-. Ki. . Jeong. Harvard University. Overview. Introduction. Current status. Examples. Future work. 2. GPU Acceleration. GPU as a fast co-processor. Massively parallel. 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. using BU Shared Computing Cluster. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. Prof. Miriam Leeser. Department of Electrical and Computer Engineering. Northeastern University . Boston, MA. mel@coe.neu.edu. Typical Radar Processing . http://www.codesourcery.com/vsiplplusplus/ssar_http://www.codesourcery.com/vsiplplusplus/ssahttp://www.codesourcery.com/vsiplplusplus/ssar_whitepaper.pdfr_whitepaper.pdfwhitepaper.pdf. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. 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. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). 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. Fyodor . Serzhenko. , . Fastvideo. , . Dubna. , Russia. Victor . Podlozhnyuk. , NVIDIA, Santa Clara, CA. © . Fastvideo. , 2011. Key Points. We implemented the fastest JPEG codec. Many applications using JPEG can benefit from our codec.