Deterministic Annealing Pairwise Clustering
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Deterministic Annealing Pairwise Clustering (dapwc) is a scalable and parallel clustering program that operate on non vector space. GitHub page for this project is available at

Success Stories


Fox, G. C. Deterministic annealing and robust scalable data mining for the data deluge. In Proceedings of the Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities (Seattle, Washington, USA, 2011). ACM. Available at


  1. Operating System
  • This program is extensively tested and known to work on,
    • Red Hat Enterprise Linux Server release 5.10 (Tikanga)
    • Ubuntu 12.04.3 LTS
    • Ubuntu 12.10
  • This may work in Windows systems depending on the ability to setup OpenMPI properly, however, this has not been tested and we recommend choosing a Linux based operating system instead.
  1. Java
  1. Apache Maven
  export MVN_HOME PATH
  1. Habanero Java (HJ) Library
  mvn install:install-file -DcreateChecksum=true -Dpackaging=jar -Dfile=habanero-java-lib-0.1.1.jar -DgroupId=habanero-java-lib -DartifactId=habanero-java-lib -Dversion=0.1.1;
  1. OpenMPI
  • We recommend using OpenMPI 1.8.1 although it works with the previous 1.7 versions. The Java binding is not available in versions prior to 1.7, hence are not recommended. Note, if using a version other than 1.8.1 please remember to set Maven dependency appropriately in the pom.xml.
  • Download OpenMPI 1.8.1 from
  • Extract the archive to a folder named openmpi-1.8.1
  • Also create a directory named build in some location. We will use this to install OpenMPI
  • Set the following environment variables
  • The instructions to build OpenMPI depend on the platform. Therefore, we highly recommend looking into the $OMPI_181/INSTALL file. Platform specific build files are available in $OMPI_181/contrib/platform directory.
  • In general, please specify --prefix=$BUILD and --enable-mpi-java as arguments to configure script. If Infiniband is available (highly recommended) specify --with-verbs=<path-to-verbs-installation>. In summary, the following commands will build OpenMPI for a Linux system.
  cd $OMPI_181
  ./configure --prefix=$BUILD --enable-mpi-java
  make;make install
  • If everything goes well mpirun --version will show mpirun (Open MPI) 1.8.1. Execute the following command to instal $OMPI_181/ompi/mpi/java/java/mpi.jar as a Maven artifact.
  mvn install:install-file -DcreateChecksum=true -Dpackaging=jar -Dfile=$OMPI_181/ompi/mpi/java/java/mpi.jar -DgroupId=ompi -DartifactId=ompijavabinding -Dversion=1.8.1
  • Few examples are available in $OMPI_181/examples. Please use mpijavac with other parameters similar to javac command to compile OpenMPI Java programs. Once compiled mpirun [options] java -cp <classpath> class-name arguments command with proper values set as arguments will run the program.

Building dapwc

  • Check all prerequisites are satisfied before building dapwc
  • Clone this git repository from Let's call this directory dapwchome
  • Once above two steps are completed, building dapwc requires only one command, mvn install, issued within dapwchome.

Note : If you have not built library locally please follow the following instructions

Please follow the following instructions to build this project with maven This is needed because of an SSL certificate issue with a dependency maven repo

execute the following commands from the root directory of the repo

keytool -import -file ./resources/ricecert/ -keystore /tmp/riceKeyStore

You can change the name of the key store and the path to it if you prefer to. This command will first ask for a password, provide any password of your choosing with at least 6 characters then it will show the following

Trust this certificate? [no]: 

type "y" and then press enter. Now the cert has been properly installed. Next use the following command to compile the code

mvn clean install

Running dapwc

The following shell script may be used with necessary modifications to run the program.


# Java classpath. This should include paths to dapwc dependent jar files and the dapwc-1.0-ompi1.8.1.jar
# The dependent jar files may be obtained by running mvn dependency:build-classpath command within dapwchome

# Obtain working directory
# Character x as a variable

# A text file listing available nodes
# Number of nodes
# Number of cores per node

# Options for Java runtime
jopts="-Xms64M -Xmx64M"

# Number of threads to use within one dapwc process
# Number of processes per node
# Total parallelism expressed as a pattern TxPxN
# where T is number of threads per process, P is processes per node, and N is total nodes

echo "Running $pat on `date`" >> status.txt
# Invoke MPI to run dapwc
mpirun --report-bindings --mca btl ^tcp --hostfile $hostfile --map-by node:PE=$(($corespernode / $ppn)) -np $(($nodes*$ppn)) java $jopts -cp $cp edu.indiana.soic.spidal.dapwc.Program -c config$ -n $nodes -t $tpn | tee $pat/pwc-out.txt
echo "Finished $pat on `date`" >> status.txt

The arguments listed in the mpirun command fall into three categories.

  • OpenMPI Runtime Parameters
    • --report-bindings requests OpenMPI runtime to output how processes are mapped to processing elements (cores) in the allocated nodes.
    • --mca btl ^tcp instructs to enable transports other than tcp, which is useful when running on Infiniband.
    • --hostfile indicates the file listing available nodes. Each node has to be a in a separate line.
    • --map-by node:PE=$(($corespernode / $ppn)) controls process mapping and binding. This is a topic on its own right, but the specific values in this example requests processes to be mapped by node while binding each to corespernode/ppn number of processing elements. A good set of slides on this topic is available at
    • -np $(($nodes*$ppn)) determines the total number of processes to run and in this case it is equal to nodes*ppn
  • Java Runtime Parameters
    • $jopts in this case lists initial and maximum heap sizes for a JVM instance.
    • -cp indicates paths to find required classes where each entry is separated by a : (in Linux)
  • Program (dapwc) Parameters
    • -c points to the configuration file. This is a Java properties files listing values for each parameter that dapwc requires. Details on these parameters will follow in a later section.
    • -n indicates the total number of nodes
    • -t denotes the number of threads to use within one instance of dapwc

Configuring dapwc

The following table summarizes the parameters used in dapwc.

Parameter Description Default Value Type
ClusterFile Path to output cluster results. n/a String
DistanceMatrixFile Path to pairwise distance file. n/a String
AddMdsFile n/a String
ClusterNumberFile n/a String
CenterPlotFile n/a String
LabelFile n/a String
TimingFile Path to output timing information. n/a String
SummaryFile Path to output summary file. n/a String
NumberDataPoints Total number of data points n/a Integer
ProcessingOption Mode of operation. 0 Integer
TransformDimension Transforms input distances to the given dimension. 4 Integer
MaxNcent Maximum number of clusters to find. n/a Integer
SplitOrExpandIt 1 Integer
MPIIOStrategy 0 Integer
TooSmallToSplit 5 Integer
MinEigTest -0.01 Double
ConvergeIntermediateClusters false Boolean
WaitIterations 10 Integer
EpsiMaxChange 0.001 Double
InitialCoolingFactor 0.9 Double
FineCoolingFactor 0.99 Double
EigenValueChange 0.001 Double
EigenVectorChange 0.001 Double
IterationAtEnd 2000 Integer
ConvergenceLoopLimit 2000 Integer
FreezingLimit 0.002 Double
PowerIterationLimit 200 Integer
ContinuousClustering false Boolean
AddMds 1 Integer
CenterPointsPerCenterTypeInOuput 3 Integer
BucketFractions 0.15,0.4,0.75 String of comma separated double values
NumberOfCenters Maximum number of centers to find for each cluster. 8 Integer
DebugPrintOption 1 Integer
ConsoleDebugOutput Flag to enable console output. true Boolean
DataTypeSize Indicates the data type size in bytes for the binary distances. 2 Integer
IsBigEndian Indicates the endianness of the binary distance file. false Boolean


We like to express our sincere gratitude to Prof. Vivek Sarkar and his team at Rice University for giving us access and continuous support for HJ library. We are equally thankful to Prof. Guillermo L�pez Taboada for giving us free unrestricted access to commercially available FastMPJ MPI library, which we evaluated in an earlier internal version. We are also thankful to FutureGrid project and its support team for their support with HPC systems. Last but not least OpenMPI community deserves equal recognition for their valuable support.

We also like to thank the following companies for providing us Open Source licences for their profiler software.

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  • YourKit supports open source projects with its full-featured Java Profiler. YourKit, LLC is the creator of YourKit Java Profiler and YourKit .NET Profiler, innovative and intelligent tools for profiling Java and .NET applications