PPT-Fast GPU Histogram Analysis for Scene Post-Processing

Andy Luedke Halo Development Team Microsoft Game Studios Why do Histogram Analysis Dynamically adjust postprocessing settings based on rendered scene content Drive tone adjustments by discovering intensity levels and adjusting . 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 . and. Nathan Smith. , Science Applications International Corporation. Ray Hoare. , Concurrent EDA. Huan. -Ting Meng. and . Jianming Jin. , University of Illinois at Urbana-Champaign. Hardware Acceleration of Electromagnetic Field Profile Computation:. 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. Histograms. Histogram Equalization. Histogram equalization is a powerful point processing enhancement technique that seeks to optimize the contrast of an image at all points. . As the name suggests, histogram equalization seeks to improve image contrast by flattening, or equalizing, the histogram of an image. . 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. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . 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. Yin “David” Yang .  . Zhenjie. Zhang. .  . Gerome . Miklau. . Prev. . Session: Marianne . Winslett. .  . Xiaokui Xiao. 1. What we talked in the last session. Privacy is a major concern in data publishing. Fast Filtering. Problems in Computer Vision. Computer Vision in One Slide. 1) Extract some features from some images . 2) Use these to formulate some . (hopefully linear) constraints. 3) Solve a . system of . 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 . . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. Paris, 2016-01-26. 2. Contents. Introduction . Brief review of ongoing IAC Adaptive Optics projects. Summary of control technologies used . Technologies comparison . C. onclusions. 3. Contents. Introduction. Multipole. . Methods. Qi Hu, Nail A. Gumerov, Ramani Duraiswami. Institute for Advanced Computer Studies . Department of Computer Science. University of Maryland, College Park, MD. Previous work. FMM .