Difference between revisions of "CSC352 Class Page 2010"
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==Cloud Computing== | ==Cloud Computing== | ||
| + | ===Literature=== | ||
* Dean, J., and S. Ghemawat, [http://labs.google.com/papers/mapreduce-osdi04.pdf MapReduce: Simplified Data Processing on Large Clusters], Dec. 2004, ([[media:MapReduce1204.pdf|cached copy]]) | * Dean, J., and S. Ghemawat, [http://labs.google.com/papers/mapreduce-osdi04.pdf MapReduce: Simplified Data Processing on Large Clusters], Dec. 2004, ([[media:MapReduce1204.pdf|cached copy]]) | ||
* Czajkowski G., [http://googleblog.blogspot.com/2008/11/sorting-1pb-with-mapreduce.html Sorting 1 PB with MapReduce], Nov. 2008, ([[media:Sorting1PBWithMapReduce.pdf|cached copy]]) | * Czajkowski G., [http://googleblog.blogspot.com/2008/11/sorting-1pb-with-mapreduce.html Sorting 1 PB with MapReduce], Nov. 2008, ([[media:Sorting1PBWithMapReduce.pdf|cached copy]]) | ||
* Armbrust M, ''et al'', [http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf Above the Clouds: A Berkeley View of Cloud Computing], Tech Rep. CB/EECS-2009-28, Feb. 2009 ([[media:AboveTheCloudsBerkeley.pdf|cached copy]]) | * Armbrust M, ''et al'', [http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf Above the Clouds: A Berkeley View of Cloud Computing], Tech Rep. CB/EECS-2009-28, Feb. 2009 ([[media:AboveTheCloudsBerkeley.pdf|cached copy]]) | ||
| + | ===Class Material=== | ||
| + | * .University of Washington: Problem Solving on Large Scale Clusters: http://code.google.com/edu/submissions/uwspr2007_clustercourse/listing.html | ||
| + | : 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. | ||
| + | ===Software=== | ||
| + | * Hadoop at Google: http://code.google.com/edu/parallel/tools/hadoopvm/index.html | ||
| + | : 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 | ||
Revision as of 21:04, 2 December 2009
Contents
Python Threads
XGrid Programming
Cloud Computing
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)
- 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
Cloud Computing
Literature
- 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)
Class Material
- .University of Washington: Problem Solving on Large Scale Clusters: http://code.google.com/edu/submissions/uwspr2007_clustercourse/listing.html
- 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.
Software
- Hadoop at Google: http://code.google.com/edu/parallel/tools/hadoopvm/index.html
- 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
