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

Author : adah | Published Date : 2023-06-26

Adapted from a talk by Sapna Jain amp R Gokilavani Some slides taken from Jingren Zhous talk on Scope isgicsucieduslidesMicrosoftSCOPEpptx Mapreduce framework

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "SCOPE Easy and Efficient Parallel Proc..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

SCOPE Easy and Efficient Parallel Processing of Massive Data Sets: Transcript

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. Scope and order of precedence This DJUHHPHQW5734757355WKH5734757523DWD573473URFHVVLQJ57347JUHHPHQW575245735657347DSSOLHV57347WR 2UDFOH57526V rocessing of Personal Data provided to Oracle by Customer as part of Oracle 57526V provision of ORXG573476HU Jingren Zhou. Microsoft Corp.. Large-scale Distributed Computing. Large data centers (x1000 machines): storage and computation. Key technology for search . (Bing, Google, Yahoo). Web data analysis, user log analysis, relevance studies, etc.. Aapo. Kyrola. Danny. Bickson. A Framework for Machine Learning and Data Mining in the Cloud . Joseph. Gonzalez. Carlos. Guestrin. Joe. Hellerstein. Big Data is Everywhere. 72 Hours . a Minute. YouTube. 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. Wolfgang Aigner. Silvia Miksch. Helwig Hauser. Radial. Sets:. Interactive Visual Analysis. of Large Overlapping Sets. Euler Diagrams. Limited scalability. Potentially overlaps. Drawability not guaranteed. 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 . Bickson. A Framework for Machine Learning and Data Mining in the Cloud . Joseph. Gonzalez. Carlos. Guestrin. Joe. Hellerstein. Big Data is Everywhere. 72 Hours . a Minute. YouTube. 28 . Million . Wikipedia Pages. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. 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? . Pipelining. Instruction Pipeline. Pipeline Hazards and their solution. Array and Vector Processing. Pipelining and Vector Processing. Parallel Processing. It refers to techniques that are used to provide simultaneous data processing.. Mohammadhossein . Behgam. Agenda. Need for parallelism. Challenges. Image processing algorithms. Data handling & Load Balancing. Communication cost & performance. What is the problem?. Image Processing applications can be very computationally demanding due to:.

Download Document

Here is the link to download the presentation.
"SCOPE Easy and Efficient Parallel Processing of Massive Data Sets"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.