PPT-Efficient Lists Intersection by CPU-GPU

Cooperative Computing Di Wu Fan Zhang Naiyong Ao Gang Wang Xiaoguang Liu Jing Liu Nankai Baidu Joint Lab Nankai University Introduction Cooperative Model GPU Batching Algorithm. Exact Data sources consumer data from national database with approximately 210 million names, postal addresses, and telephone numbers, with approximately 700 selects, originating from over 2,000 different sources. Exact Data overlays its permission compliant, opt-in email address database from over 100 sources on that national database, to make what we believe is the best, most accurate and up to date multi-channel consumer database on the market. The database is compared to the USPS National Change of Address file every 60-days, and updated as necessary. And 15 to 20 million new email addresses are acquired each month, a rigorous, proprietary hygiene process is performed, and approximately 10%, or 1.5 to 2.0 million new email addresses, are appended to the national database. Avg Access Time 2 Tokens Number of Controllers Average Access Time clock cyles brPage 16br Number of Tokens vs Avg Access Time 9 Controllers Number of Tokens Average Access Time clock cycles brPage 17br brPage 18br . 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. Game Engines & GPUs:. Current & Future. Johan Andersson. Rendering Architect. 2.5. Agenda. Goal. Share and discuss current & future graphics use cases in our games and implications for graphics hardware. Rajat Phull, . Srihari. Cadambi, Nishkam Ravi and Srimat Chakradhar. NEC Laboratories America. Princeton, New Jersey, USA.. www.nec-labs.com. OpenFOAM Overview. OpenFOAM stands for:. ‘. O. pen . F. 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. Andy Luedke. Halo Development Team. Microsoft Game Studios. Why do Histogram Analysis?. Dynamically adjust post-processing settings based on rendered scene content. Drive tone adjustments by discovering intensity levels and adjusting . Department of Geography and Planning. University at Albany. What is a GPU?. A GPU is a . graphics processing unit. Modern GPUs are composed of multiple processors. Each of these processors can perform operations similar to those of CPUs. Jia. Pan and Dinesh Manocha. University . of North Carolina, Chapel Hill, USA. http://gamma.cs.unc.edu/gplanner. Presenter: . Liangjun. Zhang, Stanford University. Real-time Motion Planning. Dynamic/uncertain/deformable environments. 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. 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. Current & Future. Johan Andersson. Rendering Architect. 2.5. Agenda. Goal. Share and discuss current & future graphics use cases in our games and implications for graphics hardware. Areas. Engine overview.