PPT-An Efficient GPGPU Implementation of

ViolaJones Classifier based Face Detection Algorithm Sharmila Shridhar Vinay Gangadhar Ram Sai Manoj ECE 759 Project Presentation Fall 2015 University of Wisconsin Madison 1 Executive Summary. B When citing this work cite the original article 201 IEEE Personal use of this material is permitted However permission to reprintrepublish this material for advertising or promotional purposes or for creating new collective works for resale or redi OBSERVATIONS FROM SOUTH AFRICA. Indran Naidoo. Deputy Director-General: Monitoring and Evaluation, Office of the Public Service Commission of South Africa. 29 June 2011. 1. OUTLINE. Country context. Entrenching M&E. 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. を協調利用する. ソフトウェア開発環境. GPGPU. 研究会. 佐藤 功人. （. 1. ）. 滝沢 寛之. （. 1. ）. 小林 広明. （. 2. ）. （１） 東北大学大学院情報科学研究科. Condor Week 2012. Bob Nordlund. Grid Computing @The Hartford…. Using Condor in our production environment since 2004. Computing Environment. Two pools (Hartford, CT and Boulder, CO). Linux central managers and schedulers. Jared . Coplin. and Martin . Burtscher. Department of Computer Science. 1. Introduction. GPU-based accelerators. Used in high-performance computing. Spreading in PCs and handheld . devices. 2. Power Characteristics of Irregular GPGPU Programs . Graphics Processing Unit (GPU). GPU is the . chip . in computer video cards, PS3, Xbox, . etc. Designed to realize the 3D graphics pipeline. Application .  Geometry  Rasterizer image. GPU development:. Christopher Fritz. CSE691, May 2015. cvfritz@buffalo.edu. Agenda. Background – fine-grained power gating. Background – GPGPU . Prior Art. Our fine-grained value-based power gating . method. Ispass. Why is GPU in the picture. Seeking . exa. -scale computing platform. Minimize . power per operation.. Power is directly correlated to the area in the processing chips.. Regular CPU. . – what are the chip areas going into?. Abbas Rahimi. ‡. , Luca . Benini. †. , Rajesh K. Gupta. ‡. ‡. UC San Diego, . †. University of Bologna . Micrel.deis.unibo.it. /. MultiTherman. Aging-Aware. Compiler-Directed VLIW Assignment for GPGPU Architectures . Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. Prepared by:. Ward Ching, Vice President, Risk Management Operations, Safeway, Inc.. Loren Nickel, FCAS, CFA, MAAA, Regional Director and Actuary, Aon Global Risk Consulting. RIMS Session ERM009. Smolyak. sparse grid with non-nested grids. Banff 2016. 1. Methodologies, . coordinates, KEO . . Spectroscopy. A. . Perveaux. . (ex doc. ). M. . Ndong. (ex post-doc). F. . Gatti. (Montpellier). . Fisica. . Nucleare. (INFN). Italy. Accelerated Computing support . in EGI-Engage. Marco.Verlato (at pd.infn.it). Task part of WP4 (JRA2): Platforms for the Data Commons. Expand the EGI federated Cloud platform with new .