'Reduce tasks' presentation slideshows

Data Intensive Cyberinfrastructure

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CS 61C: Great Ideas in Computer Architecture (formerly called Machine Structures) Map Reduce

CS 61C: Great Ideas in Computer Architecture (formerly called Machine Structures) Map Reduce. Instructor: David A. Patterson http://inst.eecs.Berkeley.edu/~cs61c/sp12. Review.

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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.

900 views • 62 slides

Hadoop Cluster & Setup

http://www.excelonlineclasses.co.nr/ excel.onlineclasses@gmail.com. Hadoop Cluster & Setup. Excel Online Classes offers following services:. Online Training Development Testing Job support Technical Guidance Job Consultancy Any needs of IT Sector. -Nagarjuna K. HADOOP CLUSTER.

673 views • 48 slides

Map Reduce

Leroy Garcia. Map Reduce. What is Map Reduce?. A patented programming model developed by Google Derived from LISP and other forms of functional programming Used for processing large data and generating large data sets Exploits large set of commodity computers

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CS525: Special Topics in DBs Large-Scale Data Management

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Distributed Systems CS 15-440

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Programming Your Network at Run-Time for Big Data Applications

Programming Your Network at Run-Time for Big Data Applications. Guohui Wang, TS Eugene Ng, Anees Shaikh Presented by Jon Logan. Objectives. Why Change Dynamically? ? Hadoop Essentials How this is accomplished SDN <-> Master Interaction Traffic Patterns Why Application Aware?

533 views • 37 slides

MapReduce Programming

MapReduce Programming. Yue-Shan Chang. User Program. (1) fork. (1) fork. (1) fork. Master. (2) assign map. (2) assign reduce. worker. split 0. (6) write. output file 0. worker. split 1. (5) remote read. (3) read. split 2. (4) local write. worker. split 3. output file 1.

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DATA ANALYTICS on web scale

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MapReduce Online

MapReduce Online. Created by: Rajesh Gadipuuri Modified by: Ying Lu. MapReduce Programming Model. Programmers think in a data-centric fashion Apply transformations to data sets The MR framework handles the Hard Stuff: Fault tolerance Distributed execution, scheduling, concurrency

576 views • 35 slides

Hadoop

Hadoop. Ida Mele. Parallel programming. Parallel programming is used to improve performance and efficiency In a parallel program, the processing is broken up into parts, that run on different machines concurrently

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Shivnath Babu

CPS216: Advanced Database Systems (Data-intensive Computing Systems) How MapReduce Works (in Hadoop). Shivnath Babu. Map function. Reduce function. Run this program as a MapReduce job. Lifecycle of a MapReduce Job. Map function. Reduce function. Run this program as a MapReduce job.

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MapReduce for Data Intensive Scientific Analyses

MapReduce for Data Intensive Scientific Analyses . Jaliya Ekanayake Shrideep Pallickara Geoffrey Fox Department of Computer Science Indiana University Bloomington, IN, 47405 . Presentation Outline. Introduction MapReduce and the Current Implementations Current Limitations Our Solution

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MapReduce: Simplified Data Processing on Large Clusters

MapReduce: Simplified Data Processing on Large Clusters. This presentation is based on the paper of the same title by Jeffrey Dean and Sanjay Ghemawat published by Google, Inc. These slides were created for CMSC818R University of Maryland. Outline. Programming Model Word Count

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Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters

Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters. Presented by : Mohammed Ali Alawi Shehab. Outlines . Introduction Heterogeneous & Homogeneous databases Map-Reduce Map-Reduce-Merge Optimizations Enhancements Conclusions. Introduction.

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Homework 2

Homework 2. In the docs folder of your Berkeley DB, have a careful look at documentation on how to configure BDB in main memory. A new description of Homework 2 is posted.

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DisCo : Distributed Co-clustering with Map-Reduce

DisCo : Distributed Co-clustering with Map-Reduce. Spiros Papadimitriou Jimeng Sun IBM T.J. Watson Research Center Hawthorne, NY, USA Reporter: Nai-Hui , Ku. Outline. Introduction Related Work Distributed Mining Process Co-clustering Huge Datasets Experiments Conclusions.

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MapReduce : Simplified Data Processing on Large Clusters

MapReduce : Simplified Data Processing on Large Clusters. ????? ???. ?? 1. MapReduce 2. Implementation 3. Refinements 4. Performance 5. Experience 6. Conclusions References. 2. 5. 1. 3. 4. 1. MapReduce 1) What is the MapReduce ?. 2. 5. 1. 3. 4. 1. MapReduce

367 views • 15 slides

MapReduce : Simplified Data Processing on Large Clusters

Jonathan Light. MapReduce : Simplified Data Processing on Large Clusters. Contents. Abstract Introduction Locality Task Granularity Backup Tasks Questions. Abstract: MapReduce. MapReduce processes and generates large data sets

238 views • 13 slides