============================================================================== Muster: Massively Scalable Clustering by Todd Gamblin [email protected]

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.

Documentation

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.

License

See the LICENSE file for license and distribution information.

Building and Installing

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

Contributors

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