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 Muster: Massively Scalable Clustering
 by Todd Gamblin
 tgamblin@llnl.gov
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The Muster library provides implementations of serial and parallel K-Medoids 
clustering algorithms.  It is intended as a general framework for parallel 
cluster analysis, particularly for performance data analysis on systems with 
very large numbers of processes.

The parallel implementations in the Muster are designed to perform well even
in environments where the data to be clustered is entirely distributed.  For
example, many performance tools need to analyze one data element from each
process in a system.  To analyze this data efficiently, clustering algorithms 
that move as little data as possible are required.  In Muster, we exploit 
sampled clustering algorithms to realize this efficiency.

The parallel algorithms in Muster are implemented using the Message Passing 
Interface (MPI), making them suitable for use on many of the world's largest 
supercomputers.  They should, however, also run efficiently on your laptop.
  
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 Documentation 
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More extensive documentation for Muster is available through the Doxygen
documentation system.  You can find the most recent auto-generated 
Muster documentation online at:
    
    http://tgamblin.github.com/muster

Alternately, you can build the documentation yourself.  Doxygen is available 
for download from:

    http://www.doxygen.org

Once you have installed Doxygen on your system, you can generate documentation
for Muster by simply running doxygen in the project root directory, e.g.:

    $ tar xzf muster.tar.gz
    $ cd muster
    $ doxygen

This will generate html documentation in doc/html. Open doc/html/index.html 
in your favorite web browser to view the documentation.


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 License
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See the LICENSE file for license and distribution information.


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 Building and Installing
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Information on building and installing Muster can be found in the INSTALL file.


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 Contributors
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Thanks to these people and organizations for their contributions (code and 
otherwise) to Muster:

  Martin Schulz         Lawrence Livermore National Laboratory
  Bronis de Supinski    Lawrence Livermore National Laboratory
  Juan Gonzalez         Barcelona Supercomputing Center
  Rob Fowler            Renaissance Computing Institute
  Daniel A. Reed        Microsoft Research