PPT-Particle Systems on GPU

Youngho Kim CIS665 GPU Programming Building a Million Particle System Lutz Latta UberFlow A GPUbased Particle Engine Peter Kipfer et al RealTime Particle Systems on the GPU in Dynamic Environments Shannon Drone. . 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. Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . 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 . 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. 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. M. Zollhöfer, E. Sert, G. Greiner and J. Süßmuth. Computer Graphics Group, University Erlangen-Nuremberg, Germany. Motivation/Requirements. Intuitive modeling. Handle-based. Direct manipulation. 2. 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. Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . 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. Current Goal(s):. Generate . stacktraces. of GPU executions and associate GPU call chains with CPU call graphs. Particular interest on how to determine call chains when in-lined GPU functions are used.