December 2017
Intermediate to advanced
718 pages
23h 14m
English
Content preview from High Performance ComputingBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 19
MapReduce
Abstract
One of the most widely used parallel programming models today is MapReduce. MapReduce is easy both to learn and use, and is especially useful in analyzing large datasets. While it is not suitable for several classes of scientific computing operations that are better served by message-passing interface or OpenMP, such as numerical linear algebra or finite element and finite difference computations, MapReduce's utility in workflows frequently called “big data” has made it a mainstay in high performance computing. This chapter introduces the MapReduce programming model and the Hadoop open-source framework which supports it.
Keywords
Hadoop; hdfs; K-means clustering; Map; MapReduce; Reduce; Scatter; Shared neighbors; Word ...
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