CSC352 Class Page 2010

Resources: References & Bibliography

Parallel Processing/Good background information

• Asanovic K. et al, The Landscape of Parallel Computing Research: A View from Berkeley, Dec. 2006. (cached copy)
• Xen
  • Mauer, R., Xen Virtualization and Linux Clustering, Linux Journal January 12th, 2006
  • Barham P., et al., Xen and the Art of Virtualization, University of Cambridge Computer Laboratory 15 JJ Thomson Avenue, Cambridge, UK, CB3 0FD
• AMD News
  • Hardwidge, B., AMD plans supercomputer with 1,000 GPUs, Jan. 2009, bit-tech.net (or graphics goes to the clouds!)
  • Halfacree G., AMD supercomputer tops TOP500 list, November 2009, bit-tech.net (or Intel gets a black eye!)

Python

• Norman Matloff and Francis Hsu's Tutorial on Python Threads (University of California, Davis) (cached copy)
• Understanding Threading in Python, Krishna G Pai, Linux Gazette, Oct. 2004
• Thread Objects from Python.Org

XGrid

• | Programming Examples, Setup, and References

• What's an XGrid system?
  • A Video presentation of the XGrid (click on movie reel icon to start).
  • A very good overview of the XGrid from macdevcenter.com

Cloud Computing

Literature

• 
  Hadoop, the definitive guide, Tim White, O'Reilly Media, June 2009, ISBN 0596521979. The Web site for the book is http://www.hadoopbook.com/ (with the data used as examples in the book)
• Dean, J., and S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, Dec. 2004, (cached copy)
• Czajkowski G., Sorting 1 PB with MapReduce, Nov. 2008, (cached copy)
• Armbrust M, et al, Above the Clouds: A Berkeley View of Cloud Computing, Tech Rep. CB/EECS-2009-28, Feb. 2009 (cached copy)
• Olson C. et. al., Pig Latin: A Not-So-Foreign Language for Data Processing, SIGMOD’08, June 9–12, 2008, Vancouver, BC, Canada.

Class Material on the Web

• University of Washington: Problem Solving on Large Scale Clusters:

The University of Washington ran an upper-division course on Distributed Computing with MapReduce in Spring 2007. Below you'll find the materials that were used for the class: five lectures in powerpoint format, as well as four lab exercises designed which were completed by students over the duration of the course, using a cluster running Hadoop.

• Distributed Systems Course at Brandeis U.
  • Introduction to Hadoop Lab
  • Single Node setup Lab
  • Hadoop Example Program Lab
  • Hadoop Cluster Setup Lab

Software/Web Links

• Apache's Documentation on Hadoop Common
  • The Hadoop Tutorial from Apache. A must-do!
  • Hadoop Streaming, i.e. using Hadoop with Python, for example.
• The HadoopBook Web site.
• The Hadoop Wiki, the authoritative source on working with Hadoop
• Hadoop at Google:

Setting up a Hadoop cluster can be an all day job. However, if you want to experiment with the platform right now, [Google] has created a virtual machine image with a preconfigured single node instance of Hadoop

• Guide for setting up IBM's Eclipse Tools for Hadoop (go to bottom of page)

The IBM MapReduce Tools for Eclipse Plug-in is a robust plug-in that brings Hadoop support to the Eclipse platform. Features include server configuration, support for launching MapReduce jobs and browsing the distributed file system. This setup assumes that you are running Eclipse (version 3.3 or above) on your computer.

• Configuring Eclipse for Hadoop

A video from Cloudera on setting up Hadoop... not easy to follow...

Videos

• A video of Tom White, author of O'Reilly's Hadoop guide, on BlipTV. White outlines the suite of projects centered around Hadoop ( an open source Map / Reduce project)