PPT-SCOPE Easy and Efficient Parallel Processing of Massive Data Sets

Adapted from a talk by Sapna Jain amp R Gokilavani Some slides taken from Jingren Zhous talk on Scope isgicsucieduslidesMicrosoftSCOPEpptx Mapreduce framework Good abstraction of groupbyaggregation operations. Copyright © 2014 by ScaleOut Software, Inc.. Portland Big Data Users Group. October 23, 2014. Bill . Bain, CEO . (. wbain@scaleoutsoftware.com). What Is Operational Intelligence?. Example: Tracking Cable Viewers. Emil . Björnson. ‡*. , . Michail . Matthaiou. ‡§. , and . Mérouane. . Debbah. ‡. ‡. Alcatel-Lucent Chair on Flexible Radio, . Supélec. , France. *. Dept. Signal Processing, KTH, and Linköping University, Sweden. CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact. Dr. Guy Tel-. Zur. Lecture 10. Agenda. Administration. Final presentations. Demos. Theory. Next week plan. Home assignment #4 (last). Final Projects. Next Sunday: Groups 1-1. 6. will present. Next Monday: Groups 1. Lee-Ad Gottlieb Hebrew U.. Aryeh Kontorovich Ben Gurion U.. Robert Krauthgamer Weizmann Institute. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. Anthony Waterman. Topics to Discuss. Are online games . c. onceptually. . p. arallel?. What portions of a game benefit from parallelization?. Graphics Processing Units (GPUs) . General-Purpose . C. omputing . Acknowledgement. G. . . Ancellet. , O. R. . Cooper, J. . Cuesta, G. . Dufour. , F. . Ebojie. , G. Huang, S. S. . Kulawik. , B. Latter, T. Leblanc, J. Liu, X. Liu, J. . Neu. , H. . Petetin. , I. . Petropavlovskikh. Max . Nanao. Automatic Processing – why?. +Rapid feedback to user on data quality. +Enables “value added” services. . +MR phasing. . +Ligand fitting. . +Automatic SAD. +QA for us. Page . 2. Automatic processing at ESRF, History. Limit Sets - groups monitoring & reporting requirements for each Permitted Feature. Limit Sets typically apply during particular operating conditions such as:. Summer vs Winter. High production volume vs low production volume. Rodrigo . C. de . Lamare. CETUC. , PUC-Rio, Brazil. Communications . Research Group, . Department . of Electronics, University of York, U.K.. delamare@cetuc.puc-rio.br. . Outline. Introduction. Application . Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Mid-point feedback. Are you learning from the papers we are reading?. Do you find class discussions helpful?. Does preparing for the class presentation help? . What Students Will Do:. Discuss requirements with clinical collaborator. – Design solution. – Fabricate solution. – Test solution in simple model. – Redesign until satisfactory. Deliverables:. Fall 2015. Lars Ailo Bongo (larsab@cs.uit.no). Course topics. Parallel programming. The parallelization process. Optimization of parallel programs. Performance analysis. Data-intensive computing. Parallel programs. By:. Dasari Charithambika (210302). Divya Gupta(210353). Course Instructors:. Dr. Preeti Malakar. Dr. Soumya Dutta.. M. Larsen, S. Labasan, P. Navrátil,. J.S. Meredith, and H. Childs (2015). Various hardware architectures are used in supercomputers, including GPUs, many-core coprocessors, large multi-core CPUs, low-power architectures, hybrid designs, and experimental designs..