'Reduce phase' presentation slideshows

A New Parallel Framework for Machine Learning

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CSC 536 Lecture 3

CSC 536 Lecture 3. Outline. Akka example: mapreduce Distributed transactions. MapReduce Framework: Motivation. Want to process lots of data ( > 1 TB) Want to parallelize the job across hundreds/thousands of commodity CPUs connected by a commodity networks

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Hybrid Cloud and Cluster Computing Paradigms for Scalable Data Intensive Applications

Hybrid Cloud and Cluster Computing Paradigms for Scalable Data Intensive Applications. Judy Qiu xqiu@indiana.edu http://salsahpc.indiana.edu School of Informatics and Computing Indiana University. April 15, 2011 University of Alabama. Challenges for CS Research.

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CS61A Lecture 30 MapReduce

CS61A Lecture 30 MapReduce. Jom Magrotker UC Berkeley EECS August 8, 2012. Slides developed from those of John DeNero , Paul Hilfinger , and Eric Tzeng. Computer Science in the News. http:// www.sciencedaily.com/releases/2012/08/120806094053.htm

738 views • 53 slides

MapReduce

MapReduce. Concurrency for data-intensive applications. MapReduce. Jeff Dean. Sanjay Ghemawat. Motivation. Application characteristics Large/massive amounts of data Simple application processing requirements Desired portability across variety of execution platforms. Execution platforms.

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http://www.excelonlineclasses.co.nr/ excel.onlineclasses@gmail.com. http://www.excelonlineclasses.co.nr/. Excel Online Classes offers following services:. Online Training Development Testing Job support Technical Guidance Job Consultancy Any needs of IT Sector.

432 views • 30 slides

MROrder: Flexible Job Ordering Optimization for Online MapReduce Workloads

MROrder: Flexible Job Ordering Optimization for Online MapReduce Workloads. Shanjiang Tang , Bu-Sung Lee, Bingsheng He. School of Computer Engineering Nanyang Technological University. 30 th Aug 2013. OutLine. Background & Motivations MROrder Evaluation Conclusion.

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Tarazu Optimizing MapReduce On Heterogeneous Clusters

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Starfish: A Self-tuning System for Big Data Analytics

Starfish: A Self-tuning System for Big Data Analytics. Motivation. As the Hadoop ecosystem has evolved, it has become accessible to a wider and less technical audience. E.g., data analysts as opposed to programmers However, creating optimal configurations by hand is entirely infeasible

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MapReduce in Hadoop Framework

MapReduce serves two essential functions: it filters and parcels out work to various nodes within the cluster or map, a function sometimes referred to as the mapper, and it organizes and reduces the results from each node into a cohesive answer to a query, referred to as the reducer.

142 views • 3 slides