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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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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.
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A demo of an image processing application running in a cloud test bed. More information at: http://www.hpl.hp.co m/open_innovation/cl oud_collaboration/
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This video showcases a parallel video processing application running on a cloud computing platform. More information at: http://www.hpl.hp.co m/open_innovation/cl oud_collaboration/
A demo of an image processing application running in a cloud test bed. More information at: http://www.hpl.hp.co m/open_innovation/cl oud_collaboration/