CSinParallel 1: Using Map-Reduce to Teach Parallel Programming Concepts Across the CS Curriculum

Part 1 -- Fundamentals; Introductory Courses

This first half of the workshop introduces map-reduce computing through the WebMapReduce (WMR)simplified interface to Hadoop, then shares our experience teaching map-reduce and related concepts of parallel and distributed computing to students in introductory sequences.

1. Presentation (7:00pm)
   
   
   • Welcome; goals.
     
   • Introduction to map-reduce computing; demo of WebMapReduce (WMR)
     
     Resources: Why teach map-reduce with WMR?; WMR users guide, including languages included (so far)
     
   • Teaching map-reduce with WMR in the introductory sequence: materials, strategies, and experience
     
     Resources: Module; direct link to teaching materials
     
2. Hands-on exercises (7:30pm)
   
   
   • Getting started with WMR
     
   • Introductory exercises
     
     Resources: Intro to WMR module ; see Using WMR , then Counting words with WMR (Python) Data sets on HDFS: /shared/gutenberg/CompleteShakespeare.txt, AnnaKarenina.txt, WarAndPeace.txt; /shared/gutenberg/all/group8
     
     Alternative explorations: WMR code examples in various languages; More WMR code examples
     
3. Break (8:15pm)
   

Part 2 -- Intermediate and Advanced Courses

This part of the workshop uses WMR to explore use of map-reduce computing in more advanced courses, and examines the relationship between the WMR interface and the Hadoop computations it performs.

1. Presentation (8:30pm)
   
   
   • WebMapReduce and its architecture; obtaining and installing WMR
     
     Resources: WMR sourceforge site; admin page
     
   • Examples: using WMR and map-reduce in upper-division (undergraduate) courses and projects. Appropriate uses of map-reduce.
     
     Resources: Module, Concurrency and Map-Reduce Strategies in Various Programming Languages
     
   • Overview of the Hadoop implementation of map-reduce.
     
     Resources: Hadoop code for word count ; Hadoop documentation
     
2. Hands-on exercises (9:00)
   
   
   • Assortment of WMR exercises
     
     Resources: Intro to WMR module ; see WMR Activities Movie Data Data sets on HDFS: /shared/MovieLens2, /shared/gutenberg/CompleteShakespeare.txt, AnnaKarenina.txt, WarAndPeace.txt; /shared/gutenberg/all/group8; and code examples in various languages
     
3. Discussion and feedback (9:40)
   
   Please complete our own short survey for grant assessment purposes.
   

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Abstract

Map-reduce, the cornerstone computational framework for cloud computing applications, has star appeal to draw students to the study of parallelism. Participants will carry out hands-on exercises designed for students at CS1/intermediate/advanced levels that introduce data-intensive scalable computing concepts, using WebMapReduce (WMR), a simplified open-source interface to the widely used Hadoop map-reduce programming environment. WMR supports programming in a choice of languages (including Java, Python, C++, C#, Scheme). Workshop includes brief introduction to direct Hadoop programming. Workshop materials will reside on csinparallel.org, along with WMR software. Intended audience: CS instructors. Laptop required (Windows, Mac, or Linux).