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Lecture 1 in a five part series introducing mapreduce and cluster computing. See http://code.google.c om/edu/content/submi ssions/mapreduce-min ilecture/listing.htm l for slides and other resources.
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Lecture 2: The MapReduce programming model. See
http://code.googl e.com/edu/content/su bmissions/mapreduce- minilecture/listing. html for slides and other resources.
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Lecture 3: The Google File System. See
http://code.googl e.com/edu/content/su bmissions/mapreduce- minilecture/listing. html for slides and other resources.
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Lecture 4: Clustering Algorithms with MapReduce. See
http://code.googl e.com/edu/content/su bmissions/mapreduce- minilecture/listing. htmlfor slides and other resources.
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Lecture 5: Parallel Graph Algorithms with MapReduce. See
http://code.googl e.com/edu/content/su bmissions/mapreduce- minilecture/listing. html for slides and other resources.
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Listen to an excerpt from Barry at the Seattle Conference on Scalability about using Map-Reduce with Large Geographic Databases. To view the full video of this event visit http://youtube.com/w atch?v=PuHv5Uz22W8
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In October 2007, Google announced that it was partnering with IBM to provide largescale cluster computing resources to undergraduate computer science students along with a creative commons licensed curriculum. Using the cluster and curriculum as a starting point, students have been able to develop some compelling projects.