PPT-Scalable SIMD-Efficient Graph Processing on GPUs

Author : liane-varnes | Published Date : 2017-06-01

Farzad Khorasani Rajiv Gupta Laxmi N Bhuyan University of California Riverside Scalable SIMDEfficient Graph Processing on GPUs Graph Processing Building blocks

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Scalable SIMD-Efficient Graph Processing on GPUs: Transcript

Farzad Khorasani Rajiv Gupta Laxmi N Bhuyan University of California Riverside Scalable SIMDEfficient Graph Processing on GPUs Graph Processing Building blocks of data analytics. Improving SIMD for 2015. Alastair McAlpine. Head of Local Outcomes Team. Office of Chief Statistician and Performance. Scottish Government. Facilitators. Cath Dickie. Ian Volante. Gregor Boyd. Victoria Kinnear-Lachhab. David . Kaser. Lecture Series. Lorcan Dempsey / . @. LorcanD. Indiana University, . 7 October 2012. How terrific to see you are the featured lecturer this year. Just thought I'd mention that David . Commutativity. Rule: Designing Scalable Software for Multicore Processors. Austin T. Clements, M. . Frans. . Kaashoek. , . Nickolai. . Zeldovich. , Robert T. Morris, and Eddie Kohler. MIT CSAIL and Harvard University. Processors. Presented by . Remzi. Can . Aksoy. *Some slides . are. . borrowed from a ‘Papers We Love’ . Presentation. EECS 582 – F16. 1. Outline. The . Scalable Commutativity Rule: . Whenever interface operations commute, they can be implemented in a way that scales. Or, How much wood could a woodchuck chuck if a woodchuck could chuck . n. pieces of wood in parallel?. Wojtek Rajski, Nels Oscar, David Burri, Alex Diede. Introduction. We have seen how to improve performance through exploitation of:. GPU. Radeon. . R700. (HD . 4xxx. ) series. Elizabeth . Soechting. David Chang. Jessica Vasconcellos. 1. CS 433 . Advanced Computer Architecture. May 7. , 2008. General. 2. General. Interconnect. SIMD. Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. Underlying Hardware Parallelism. Jiecao Yu. 1. , Andrew Lukefahr. 1. , David Palframan. 2. , Ganesh Dasika. 2. ,. Reetuparna. Das. 1. , Scott Mahlke. 1. 1. University of Michigan . –. Ann Arbor . on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Using improvement methodology from the Children and Young People Improvement Collaborative to lever whole school commitment to closing the gap. Children and Young Peoples Improvement Collaborative (formerly . Bin . Ren. Advisor: Dr. . Gagan. . Agrawal. 4/6/2014. 1. Streaming SIMD Extension. Motivation. 4/6/2014. 2. Various emerging parallel architectures. Intel . . SSE. 1999. Intel . . SSE3. 2004. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. & Plan. Lundavra Primary – . Fort William. Lundavra Primary – New build school - opened August 2015. Amalgamation of three existing schools. 11 classroom school and large Nursery. Serves the Fort William town area .

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