From 0f18e8a0308f0941c9eefd9a8933ac8d15f32aee Mon Sep 17 00:00:00 2001
From: yeom <yeom>
Date: Fri, 29 Jan 2010 00:18:06 +0000
Subject: [PATCH] kmeans benchmark.

---
 Robust/src/Benchmarks/mlp/kmeans/Cluster.java |   193 +
 Robust/src/Benchmarks/mlp/kmeans/Common.java  |   129 +
 .../src/Benchmarks/mlp/kmeans/GlobalArgs.java |    80 +
 Robust/src/Benchmarks/mlp/kmeans/KMeans.java  |   454 +
 .../src/Benchmarks/mlp/kmeans/LICENSE.kmeans  |    36 +
 Robust/src/Benchmarks/mlp/kmeans/Normal.java  |   324 +
 Robust/src/Benchmarks/mlp/kmeans/README       |    89 +
 Robust/src/Benchmarks/mlp/kmeans/Random.java  |   102 +
 .../Benchmarks/mlp/kmeans/RandomExact.java    |    87 +
 Robust/src/Benchmarks/mlp/kmeans/inputs/color |   Bin 0 -> 637028 bytes
 .../src/Benchmarks/mlp/kmeans/inputs/color100 |   100 +
 Robust/src/Benchmarks/mlp/kmeans/inputs/edge  |   Bin 0 -> 1274048 bytes
 .../src/Benchmarks/mlp/kmeans/inputs/edge100  |   100 +
 .../Benchmarks/mlp/kmeans/inputs/generate.py  |    41 +
 .../mlp/kmeans/inputs/random-n150-d16-c16.txt |    50 +
 .../kmeans/inputs/random-n16384-d24-c16.txt   | 16384 ++++
 .../kmeans/inputs/random-n2048-d16-c16.txt    |  2048 +
 .../kmeans/inputs/random-n65536-d32-c16.txt   | 65536 ++++++++++++++++
 .../mlp/kmeans/inputs/random10000_12          | 10000 +++
 .../mlp/kmeans/inputs/random1000_12           |  1000 +
 .../mlp/kmeans/inputs/random50000_12          | 50000 ++++++++++++
 .../Benchmarks/mlp/kmeans/inputs/texture100   |   100 +
 Robust/src/Benchmarks/mlp/kmeans/makefile     |    44 +
 Robust/src/Benchmarks/mlp/kmeans/run          |     1 +
 24 files changed, 146898 insertions(+)
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/Cluster.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/Common.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/GlobalArgs.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/KMeans.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/LICENSE.kmeans
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/Normal.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/README
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/Random.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/RandomExact.java
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/color
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/color100
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/edge
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/edge100
 create mode 100755 Robust/src/Benchmarks/mlp/kmeans/inputs/generate.py
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random-n150-d16-c16.txt
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random-n16384-d24-c16.txt
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random-n2048-d16-c16.txt
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random-n65536-d32-c16.txt
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random10000_12
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random1000_12
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/random50000_12
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/inputs/texture100
 create mode 100644 Robust/src/Benchmarks/mlp/kmeans/makefile
 create mode 100755 Robust/src/Benchmarks/mlp/kmeans/run

diff --git a/Robust/src/Benchmarks/mlp/kmeans/Cluster.java b/Robust/src/Benchmarks/mlp/kmeans/Cluster.java
new file mode 100644
index 00000000..e35be987
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/Cluster.java
@@ -0,0 +1,193 @@
+/* =============================================================================
+ *
+ * cluster.java
+ *
+ * =============================================================================
+ *
+ * Description:
+ *
+ * Takes as input a file, containing 1 data point per per line, and performs a
+ * fuzzy c-means clustering on the data. Fuzzy clustering is performed using
+ * min to max clusters and the clustering that gets the best score according to
+ * a compactness and separation criterion are returned.
+ *
+ *
+ * Author:
+ *
+ * Brendan McCane
+ * James Cook University of North Queensland. Australia.
+ * email: mccane@cs.jcu.edu.au
+ *
+ *
+ * Edited by:
+ *
+ * Jay Pisharath, Wei-keng Liao
+ * Northwestern University
+ *
+ * Chi Cao Minh
+ * Stanford University
+ *
+ * Ported to Java:
+ * Alokika Dash
+ * University of California, Irvine
+ *
+ * =============================================================================
+ *
+ * For the license of kmeans, please see kmeans/LICENSE.kmeans
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * Unless otherwise noted, the following license applies to STAMP files:
+ * 
+ * Copyright (c) 2007, Stanford University
+ * All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ * 
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ * 
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in
+ *       the documentation and/or other materials provided with the
+ *       distribution.
+ * 
+ *     * Neither the name of Stanford University nor the names of its
+ *       contributors may be used to endorse or promote products derived
+ *       from this software without specific prior written permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY STANFORD UNIVERSITY ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL STANFORD UNIVERSITY BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+ * THE POSSIBILITY OF SUCH DAMAGE.
+*
+* =============================================================================
+*/
+
+public class Cluster {
+
+  public Cluster() {
+
+  }
+
+  /* =============================================================================
+   * extractMoments
+   * =============================================================================
+   */
+  public static float[]
+    extractMoments (float []data, int num_elts, int num_moments)
+    {
+      float[] moments = new float[num_moments];
+
+      for (int i = 0; i < num_elts; i++) {
+        moments[0] += data[i];
+      }
+
+      moments[0] = moments[0] / num_elts;
+      for (int j = 1; j < num_moments; j++) {
+        moments[j] = 0;
+        for (int i = 0; i < num_elts; i++) {
+          moments[j] = (float)(moments[j]+ Math.pow((data[i]-moments[0]), j+1));
+        }
+        moments[j] = moments[j] / num_elts;
+      }
+      return moments;
+    }
+
+
+  /* =============================================================================
+   * zscoreTransform
+   * =============================================================================
+   */
+  public static void
+    zscoreTransform (float[][] data, /* in & out: [numObjects][numAttributes] */
+        int     numObjects,
+        int     numAttributes)
+    {
+      float[] moments;
+      float[] single_variable = new float[numObjects];
+      for (int i = 0; i < numAttributes; i++) {
+        for (int j = 0; j < numObjects; j++) {
+          single_variable[j] = data[j][i];
+        }
+        moments = extractMoments(single_variable, numObjects, 2);
+        moments[1] = (float) Math.sqrt((double)moments[1]);
+        for (int j = 0; j < numObjects; j++) {
+          data[j][i] = (data[j][i]-moments[0])/moments[1];
+        }
+      }
+    }
+
+
+  /* =============================================================================
+   * cluster_exec
+   * =============================================================================
+   */
+  public static void
+    cluster_exec (
+        int      nthreads,               /* in: number of threads*/
+        int      numObjects,             /* number of input objects */
+        int      numAttributes,          /* size of attribute of each object */
+        float[][]  attributes,           /* [numObjects][numAttributes] */
+        KMeans kms,                       /* KMeans class hold the inputs and outputs */
+        GlobalArgs args                 /* Global thread arguments */
+        )
+    {
+      int itime;
+      int nclusters;
+
+      float[][] tmp_cluster_centres;
+      int[] membership = new int[numObjects];
+
+      Random randomPtr = new Random();
+      randomPtr.random_alloc();
+
+      if (kms.use_zscore_transform == 1) {
+        zscoreTransform(attributes, numObjects, numAttributes);
+      }
+
+      itime = 0;
+
+      /*
+       * From min_nclusters to max_nclusters, find best_nclusters
+       */
+      for (nclusters = kms.min_nclusters; nclusters <= kms.max_nclusters; nclusters++) {
+
+        randomPtr.random_seed(7);
+
+        Normal norm = new Normal();
+
+        tmp_cluster_centres = norm.normal_exec(nthreads,
+            attributes,
+            numAttributes,
+            numObjects,
+            nclusters,
+            kms.threshold,
+            membership,
+            randomPtr,
+            args);
+
+	kms.cluster_centres = tmp_cluster_centres;
+	kms.best_nclusters = nclusters;
+
+
+        itime++;
+      } /* nclusters */
+    }
+}
+
+/* =============================================================================
+ *
+ * End of cluster.java
+ *
+ * =============================================================================
+ */
diff --git a/Robust/src/Benchmarks/mlp/kmeans/Common.java b/Robust/src/Benchmarks/mlp/kmeans/Common.java
new file mode 100644
index 00000000..6e777eba
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/Common.java
@@ -0,0 +1,129 @@
+/* =============================================================================
+ *
+ * common.java
+ *
+ * =============================================================================
+ *
+ * For the license of bayes/sort.h and bayes/sort.c, please see the header
+ * of the files.
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of kmeans, please see kmeans/LICENSE.kmeans
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of ssca2, please see ssca2/COPYRIGHT
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of lib/mt19937ar.c and lib/mt19937ar.h, please see the
+ * header of the files.
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of lib/rbtree.h and lib/rbtree.c, please see
+ * lib/LEGALNOTICE.rbtree and lib/LICENSE.rbtree
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * Unless otherwise noted, the following license applies to STAMP files:
+ * 
+ * Copyright (c) 2007, Stanford University
+ * All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ * 
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ * 
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in
+ *       the documentation and/or other materials provided with the
+ *       distribution.
+ * 
+ *     * Neither the name of Stanford University nor the names of its
+ *       contributors may be used to endorse or promote products derived
+ *       from this software without specific prior written permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY STANFORD UNIVERSITY ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL STANFORD UNIVERSITY BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+ * THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * =============================================================================
+ */
+
+public class Common {
+
+  public Common() {
+  }
+
+
+  /* =============================================================================
+   * common_euclidDist2
+   * -- multi-dimensional spatial Euclid distance square
+   * =============================================================================
+   */
+   public static float
+    common_euclidDist2 (float[] pt1, float[] pt2, int numdims)
+    {
+      int i;
+      float ans = 0.0f;
+
+      for (i = 0; i < numdims; i++) {
+        ans += (pt1[i] - pt2[i]) * (pt1[i] - pt2[i]);
+      }
+
+      return ans;
+    }
+
+
+  /* =============================================================================
+   * common_findNearestPoint
+   * =============================================================================
+   */
+  public static int
+    common_findNearestPoint (float[]  pt,        /* [nfeatures] */
+        int     nfeatures,
+        float[][] pts,       /* [npts][nfeatures] */
+        int     npts)
+    {
+      int index = -1;
+      int i;
+      //double max_dist = FLT_MAX;
+      float max_dist = (float)3.40282347e+38f;
+      float limit = (float) 0.99999;
+
+      /* Find the cluster center id with min distance to pt */
+      for (i = 0; i < npts; i++) {
+        float dist = common_euclidDist2(pt, pts[i], nfeatures);  /* no need square root */
+        if ((dist / max_dist) < limit) {
+          max_dist = dist;
+          index = i;
+          if (max_dist == 0) {
+            break;
+          }
+        }
+      }
+
+      return index;
+    }
+}
+
+
+/* =============================================================================
+ *
+ * End of common.java
+ *
+ * =============================================================================
+ */
diff --git a/Robust/src/Benchmarks/mlp/kmeans/GlobalArgs.java b/Robust/src/Benchmarks/mlp/kmeans/GlobalArgs.java
new file mode 100644
index 00000000..10673bdc
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/GlobalArgs.java
@@ -0,0 +1,80 @@
+/* ==============================================================================
+ *
+ * GlobalArgs.java
+ * -- Class that holds all the global parameters used by each thread
+ *    during parallel execution
+ *
+ * =============================================================================
+ * Author:
+ *
+ * Alokika Dash
+ * University of California, Irvine
+ * email adash@uci.edu
+ *
+ * =============================================================================
+ */
+
+public class GlobalArgs {
+
+  public GlobalArgs() {
+
+  }
+
+  /**
+   * Number of threads
+   **/
+  public int nthreads;
+
+  /**
+   * List of attributes
+   **/
+  public float[][] feature;
+
+  /**
+   * Number of attributes per Object
+   **/
+  public int nfeatures;
+
+  /**
+   * Number of Objects
+   **/
+  public int npoints;
+
+
+  /**
+   * Iteration id between min_nclusters to max_nclusters 
+   **/
+  public int nclusters;
+
+
+  /**
+   * Array that holds change index of cluster center per thread 
+   **/
+  public int[] membership;
+
+  /**
+   *
+   **/
+  public float[][] clusters;
+
+
+  /**
+   * Number of points in each cluster [nclusters]
+   **/
+  public int[] new_centers_len;
+
+  /**
+   * New centers of the clusters [nclusters][nfeatures]
+   **/
+  public float[][] new_centers;
+
+  /**
+    *
+  **/
+  public int global_i;
+
+  public float global_delta;
+
+  long global_time;
+
+}
diff --git a/Robust/src/Benchmarks/mlp/kmeans/KMeans.java b/Robust/src/Benchmarks/mlp/kmeans/KMeans.java
new file mode 100644
index 00000000..33becff7
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/KMeans.java
@@ -0,0 +1,454 @@
+/* =============================================================================
+ *
+ * kmeans.java
+ *
+ * =============================================================================
+ *
+ * Description:
+ *
+ * Takes as input a file:
+ *   ascii  file: containing 1 data point per line
+ *   binary file: first int is the number of objects
+ *                2nd int is the no. of features of each object
+ *
+ * This example performs a fuzzy c-means clustering on the data. Fuzzy clustering
+ * is performed using min to max clusters and the clustering that gets the best
+ * score according to a compactness and separation criterion are returned.
+ *
+ *
+ * Author:
+ *
+ * Wei-keng Liao
+ * ECE Department Northwestern University
+ * email: wkliao@ece.northwestern.edu
+ *
+ *
+ * Edited by:
+ *
+ * Jay Pisharath
+ * Northwestern University
+ *
+ * Chi Cao Minh
+ * Stanford University
+ *
+ * Port to Java version
+ * Alokika Dash
+ * University of California, Irvine
+ *
+ * =============================================================================
+ *
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of kmeans, please see kmeans/LICENSE.kmeans
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * Unless otherwise noted, the following license applies to STAMP files:
+ * 
+ * Copyright (c) 2007, Stanford University
+ * All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ * 
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ * 
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in
+ *       the documentation and/or other materials provided with the
+ *       distribution.
+ * 
+ *     * Neither the name of Stanford University nor the names of its
+ *       contributors may be used to endorse or promote products derived
+ *       from this software without specific prior written permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY STANFORD UNIVERSITY ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL STANFORD UNIVERSITY BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+ * THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * =============================================================================
+ */
+
+public class KMeans /*extends Thread*/ {
+  /**
+   * User input for max clusters
+   **/
+  int max_nclusters;
+
+  /**
+   * User input for min clusters
+   **/
+  int min_nclusters;
+
+  /**
+   * Check for Binary file
+   **/
+  int isBinaryFile;
+
+  /**
+   * Using zscore transformation for cluster center 
+   * deviating from distribution's mean
+   **/
+  int use_zscore_transform;
+
+  /**
+   * Input file name used for clustering
+   **/
+  String filename;
+
+  /**
+   * Total number of threads
+   **/
+  int nthreads;
+
+  /**
+   * threshold until which kmeans cluster continues
+   **/
+  float threshold;
+
+  /**
+   * thread id
+   **/
+  int threadid;
+
+  /**
+   * Global arguments for threads 
+   **/
+  GlobalArgs g_args;
+
+  /**
+   * Output:  Number of best clusters
+   **/
+  int best_nclusters;
+
+  /**
+   * Output: Cluster centers
+   **/
+  float[][] cluster_centres;
+
+  public KMeans() {
+    max_nclusters = 13;
+    min_nclusters = 4;
+    isBinaryFile = 0;
+    use_zscore_transform = 1;
+    threshold = (float) 0.001;
+    best_nclusters=0;
+  }
+
+  public KMeans(int threadid, GlobalArgs g_args) {
+    this.threadid = threadid;
+    this.g_args = g_args;
+  }
+
+  public void run() {
+    while(true) {
+//      Barrier.enterBarrier();
+      Normal.work(threadid, g_args);
+//      Barrier.enterBarrier();
+    }
+  }
+
+  /* =============================================================================
+   * main
+   * =============================================================================
+   */
+  public static void main(String[] args) {
+    int nthreads;
+    int MAX_LINE_LENGTH = 1000000; /* max input is 400000 one digit input + spaces */
+
+    /**
+     * Read options fron the command prompt 
+     **/
+    KMeans kms = new KMeans();
+    KMeans.parseCmdLine(args, kms);
+    nthreads = kms.nthreads;
+
+    /* Initiate Barriers */
+//    Barrier.setBarrier(nthreads);
+
+    if (kms.max_nclusters < kms.min_nclusters) {
+      System.out.println("Error: max_clusters must be >= min_clusters\n");
+      System.exit(0);
+    }
+    
+    float[][] buf;
+    float[][] attributes;
+    int numAttributes = 0;
+    int numObjects = 0;
+
+    /*
+     * From the input file, get the numAttributes (columns in txt file) and numObjects (rows in txt file)
+     */
+    if (kms.isBinaryFile == 1) {
+      System.out.println("TODO: Unimplemented Binary file option\n");
+      System.exit(0);
+    }
+
+    FileInputStream inputFile = new FileInputStream(kms.filename);
+    byte b[] = new byte[MAX_LINE_LENGTH];
+    int n;
+    while ((n = inputFile.read(b)) != 0) {
+      for (int i = 0; i < n; i++) {
+        if (b[i] == '\n')
+          numObjects++;
+      }
+    }
+    inputFile.close();
+    inputFile = new FileInputStream(kms.filename);
+    String line = null;
+    if((line = inputFile.readLine()) != null) {
+      int index = 0;
+      boolean prevWhiteSpace = true;
+      while(index < line.length()) {
+        char c = line.charAt(index++);
+        boolean currWhiteSpace = Character.isWhitespace(c);
+        if(prevWhiteSpace && !currWhiteSpace){
+          numAttributes++;
+        }   
+        prevWhiteSpace = currWhiteSpace;
+      }   
+    }   
+    inputFile.close();
+
+    /* Ignore the first attribute: numAttributes = 1; */
+    numAttributes = numAttributes - 1; 
+    System.out.println("numObjects= " + numObjects + " numAttributes= " + numAttributes);
+
+    /* Allocate new shared objects and read attributes of all objects */
+    buf = new float[numObjects][numAttributes];
+    attributes = new float[numObjects][numAttributes];
+    KMeans.readFromFile(inputFile, kms.filename, buf, MAX_LINE_LENGTH);
+    System.out.println("Finished Reading from file ......");
+
+    /*
+     * The core of the clustering
+     */
+
+    int nloops = 1;
+    int len = kms.max_nclusters - kms.min_nclusters + 1;
+
+    KMeans[] km = new KMeans[nthreads];
+    GlobalArgs g_args = new GlobalArgs();
+    g_args.nthreads = nthreads;
+
+    /* Create and Start Threads */
+    for(int i = 1; i<nthreads; i++) {
+      km[i] = new KMeans(i, g_args);
+    }
+
+    for(int i = 1; i<nthreads; i++) {
+      km[i].run();
+    }
+
+    System.out.println("Finished Starting threads......");
+
+    for (int i = 0; i < nloops; i++) {
+      /*
+       * Since zscore transform may perform in cluster() which modifies the
+       * contents of attributes[][], we need to re-store the originals
+       */
+      for(int x = 0; x < numObjects; x++) {
+        for(int y = 0; y < numAttributes; y++) {
+          attributes[x][y] = buf[x][y];
+        }
+      }
+
+      Cluster.cluster_exec(nthreads,
+          numObjects,
+          numAttributes,
+          attributes,             // [numObjects][numAttributes] 
+          kms,                    //main class that holds users inputs from command prompt and output arrays that need to be filled
+          g_args);                // Global arguments common to all threads
+    }
+
+    System.out.println("TIME="+g_args.global_time);
+
+    System.out.println("Printing output......");
+    System.out.println("Best_nclusters= " + kms.best_nclusters);
+
+    /* Output: the coordinates of the cluster centres */
+    {
+      for (int i = 0; i < kms.best_nclusters; i++) {
+        System.out.print(i + " ");
+        for (int j = 0; j < numAttributes; j++) {
+          System.out.print(kms.cluster_centres[i][j] + " ");
+        }
+        System.out.println("\n");
+      }
+    }
+
+    System.out.println("Finished......");
+    System.exit(0);
+  }
+
+  public static void parseCmdLine(String args[], KMeans km) {
+    int i = 0;
+    String arg;
+    while (i < args.length && args[i].startsWith("-")) {
+      arg = args[i++];
+      //check options
+      if(arg.equals("-m")) {
+        if(i < args.length) {
+          km.max_nclusters = new Integer(args[i++]).intValue();
+        }
+      } else if(arg.equals("-n")) {
+        if(i < args.length) {
+          km.min_nclusters = new Integer(args[i++]).intValue();
+        }
+      } else if(arg.equals("-t")) {
+        if(i < args.length) {
+          km.threshold = (float) Double.parseDouble(args[i++]);
+        }
+      } else if(arg.equals("-i")) {
+        if(i < args.length) {
+          km.filename = args[i++];
+        }
+      } else if(arg.equals("-b")) {
+        if(i < args.length) {
+          km.isBinaryFile = new Integer(args[i++]).intValue();
+        }
+      } else if(arg.equals("-z")) {
+        km.use_zscore_transform=0;
+      } else if(arg.equals("-nthreads")) {
+        if(i < args.length) {
+          km.nthreads = new Integer(args[i++]).intValue();
+        }
+      } else if(arg.equals("-h")) {
+        km.usage();
+      }
+    }
+    if(km.nthreads == 0 || km.filename == null) {
+      km.usage();
+    }
+  }
+
+  /**
+   * The usage routine which describes the program options.
+   **/
+  public void usage() {
+    System.out.println("usage: ./kmeans -m <max_clusters> -n <min_clusters> -t <threshold> -i <filename> -nthreads <threads>\n");
+    System.out.println(                   "  -i filename:     file containing data to be clustered\n");
+    System.out.println(                   "  -b               input file is in binary format\n");
+    System.out.println(                   "  -m max_clusters: maximum number of clusters allowed\n");
+    System.out.println(                   "  -n min_clusters: minimum number of clusters allowed\n");
+    System.out.println(                   "  -z             : don't zscore transform data\n");
+    System.out.println(                   "  -t threshold   : threshold value\n");
+    System.out.println(                   "  -nthreads      : number of threads\n");
+  }
+
+  /**
+   * readFromFile()
+   * Read attributes from the input file into an array
+   **/
+  public static void readFromFile(FileInputStream inputFile, String filename, float[][] buf, int MAX_LINE_LENGTH) {
+    inputFile = new FileInputStream(filename);
+    int j;
+    int i = 0;
+
+    byte b[] = new byte[MAX_LINE_LENGTH];
+    int n;
+    byte oldbytes[]=null;
+
+
+    j = -1;
+    while ((n = inputFile.read(b)) != 0) {
+      int x=0;
+
+      if (oldbytes!=null) {
+	//find space
+	boolean cr=false;
+	for (;x < n; x++) {
+	  if (b[x] == ' ')
+	    break;
+	  if (b[x] == '\n') {
+	    cr=true;
+	    break;
+	  }
+	}
+	byte newbytes[]=new byte[x+oldbytes.length];
+	boolean isnumber=false;
+	for(int ii=0;ii<oldbytes.length;ii++) {
+	  if (oldbytes[ii]>='0'&&oldbytes[ii]<='9')
+	    isnumber=true;
+	  newbytes[ii]=oldbytes[ii];
+	}
+	for(int ii=0;ii<x;ii++) {
+	  if (b[ii]>='0'&&b[ii]<='9')
+	    isnumber=true;
+	  newbytes[ii+oldbytes.length]=b[ii];
+	}
+	if (x!=n)
+	  x++; //skip past space or cr
+	if (isnumber) {
+	  if (j>=0) {
+	    buf[i][j]=(float)Double.parseDouble(new String(newbytes, 0, newbytes.length));
+	  }
+	  j++;
+	}
+	if (cr) {
+	  j=-1;
+	  i++;
+	}
+	oldbytes=null;
+      }
+
+      while (x < n) {
+	int y=x;
+	boolean cr=false;
+	boolean isnumber=false;
+	for(y=x;y<n;y++) {
+	  if ((b[y]>='0')&&(b[y]<='9'))
+	    isnumber=true;
+	  if (b[y]==' ')
+	    break;
+	  if (b[y]=='\n') {
+	    cr=true;
+	    break;
+	  }
+	}
+	if (y==n) {
+	  //need to continue for another read
+	  oldbytes=new byte[y-x];
+	  for(int ii=0;ii<(y-x);ii++)
+	    oldbytes[ii]=b[ii+x];
+	  break;
+	}
+	
+	//otherwise x is beginning of character string, y is end
+	if (isnumber) {
+	  if (j>=0) {
+	    buf[i][j]=(float)Double.parseDouble(new String(b,x,y-x));
+	  }
+	  j++;
+	}
+	if (cr) {
+	  i++;//skip to next line
+	  j = -1;//don't store line number
+	  x=y;//skip to end of number
+	  x++;//skip past return
+	} else {
+	  x=y;//skip to end of number
+	  x++;//skip past space
+	}
+      }
+    }
+    inputFile.close();
+  }
+}
+
+/* =============================================================================
+ *
+ * End of kmeans.java
+ *
+ * =============================================================================
+ */
diff --git a/Robust/src/Benchmarks/mlp/kmeans/LICENSE.kmeans b/Robust/src/Benchmarks/mlp/kmeans/LICENSE.kmeans
new file mode 100644
index 00000000..e3f05491
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/LICENSE.kmeans
@@ -0,0 +1,36 @@
+IMPORTANT:  READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+By downloading, copying, installing or using the software you agree
+to this license.  If you do not agree to this license, do not download,
+install, copy or use the software.
+
+
+Copyright (c) 2006 Northwestern University
+All rights reserved.
+
+Redistribution of the software in source and binary forms,
+with or without modification, is permitted provided that the
+following conditions are met: 
+
+1       Redistributions of source code must retain the above copyright
+        notice, this list of conditions and the following disclaimer. 
+
+2       Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution. 
+
+3       Neither the name of Northwestern University nor the names of its
+        contributors may be used to endorse or promote products derived
+        from this software without specific prior written permission.
+ 
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS
+IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND
+FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
+NORTHWESTERN UNIVERSITY OR ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/Robust/src/Benchmarks/mlp/kmeans/Normal.java b/Robust/src/Benchmarks/mlp/kmeans/Normal.java
new file mode 100644
index 00000000..8efa92e4
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/Normal.java
@@ -0,0 +1,324 @@
+import Barrier;
+import Common;
+import GlobalArgs;
+import Normal;
+import Random;
+import System;
+import intwrapper;
+
+/* =============================================================================
+ *
+ * normal.java
+ * -- Implementation of normal k-means clustering algorithm
+ *
+ * =============================================================================
+ *
+ * Author:
+ *
+ * Wei-keng Liao
+ * ECE Department, Northwestern University
+ * email: wkliao@ece.northwestern.edu
+ *
+ *
+ * Edited by:
+ *
+ * Jay Pisharath
+ * Northwestern University.
+ *
+ * Chi Cao Minh
+ * Stanford University
+ *
+ * Alokika Dash
+ * University of California, Irvine
+ * Ported to Java
+ *
+ * =============================================================================
+ *
+ * For the license of bayes/sort.h and bayes/sort.c, please see the header
+ * of the files.
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of kmeans, please see kmeans/LICENSE.kmeans
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of ssca2, please see ssca2/COPYRIGHT
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of lib/mt19937ar.c and lib/mt19937ar.h, please see the
+ * header of the files.
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * For the license of lib/rbtree.h and lib/rbtree.c, please see
+ * lib/LEGALNOTICE.rbtree and lib/LICENSE.rbtree
+ * 
+ * ------------------------------------------------------------------------
+ * 
+ * Unless otherwise noted, the following license applies to STAMP files:
+ * 
+ * Copyright (c) 2007, Stanford University
+ * All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ * 
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ * 
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in
+ *       the documentation and/or other materials provided with the
+ *       distribution.
+ * 
+ *     * Neither the name of Stanford University nor the names of its
+ *       contributors may be used to endorse or promote products derived
+ *       from this software without specific prior written permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY STANFORD UNIVERSITY ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL STANFORD UNIVERSITY BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+ * THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * =============================================================================
+ */
+
+public class Normal {
+	int CHUNK;
+
+	public Normal() {
+		CHUNK = 3;
+	}
+
+	/*
+	 * ==========================================================================
+	 * === work
+	 * ==================================================================
+	 * ===========
+	 */
+	public static void work(int myId, GlobalArgs args) {
+
+    float[][] feature = args.feature;
+    int nfeatures = args.nfeatures;
+    int npoints = args.npoints;
+    int nclusters = args.nclusters;
+    int[] membership = args.membership;
+    float[][] clusters = args.clusters;
+    int[] new_centers_len = args.new_centers_len;
+    float[][] new_centers = args.new_centers;
+    float delta = 0.0f;
+    int  start, stop;
+    
+    int CHUNK=3;
+    start = myId * CHUNK;
+
+    /*
+    while (start < npoints) {
+        stop = (((start + CHUNK) < npoints) ? (start + CHUNK) : npoints);
+      	  
+      	  sese parallel{
+      		int indexArrayLen=stop-start;    		
+      		int indexArray[]=new int[indexArrayLen];
+      		int pidx=0;
+      		 for (int i = start; i < stop; i++) {
+      		   int index = Common.common_findNearestPoint(feature[i],
+      	  	            nfeatures,
+      	  	            clusters,
+      	  	            nclusters);
+      		   indexArray[pidx]=index;
+      		 pidx++;
+      		 }
+  	        
+      	  }
+      	  
+      	  sese serial{
+      		  
+      		   int sidx=0;
+      		   for (int i = start; i < stop; i++) {
+      			   
+      			    int newIndex=indexArray[sidx];
+     		        if (membership[i] != newIndex) {
+  		                delta += 1.0f;
+     		        }
+	
+	      	        membership[i] = newIndex;
+	
+	      	          new_centers_len[newIndex] = new_centers_len[newIndex] + 1;
+	      	          for (int j = 0; j < nfeatures; j++) {
+	      	            new_centers[newIndex][j] = new_centers[newIndex][j] + feature[i][j];
+	      	          }
+	      	          
+	      	        sidx++;
+      		   }
+
+      	  }
+
+
+        if (start + CHUNK < npoints) {
+//  	atomic {
+            start = args.global_i;
+            args.global_i = start + CHUNK;
+//          }
+        } else {
+          break;
+        }
+      }
+*/
+    
+
+
+    while (start < npoints) {
+      stop = (((start + CHUNK) < npoints) ? (start + CHUNK) : npoints);
+
+      for (int i = start; i < stop; i++) {
+    	  
+    	  sese parallel{
+	        int index = Common.common_findNearestPoint(feature[i],
+	            nfeatures,
+	            clusters,
+	            nclusters);
+    	  }
+    	  
+    	  sese serial{
+    		  
+		         // If membership changes, increase delta by 1.
+		         // membership[i] cannot be changed by other threads
+		        if (membership[i] != index) {
+		            delta += 1.0f;
+		        }
+
+		        // Assign the membership to object i 
+    	        // membership[i] can't be changed by other thread 
+    	        membership[i] = index;
+
+    	        // Update new cluster centers : sum of objects located within 
+    	          new_centers_len[index] = new_centers_len[index] + 1;
+    	          for (int j = 0; j < nfeatures; j++) {
+    	            new_centers[index][j] = new_centers[index][j] + feature[i][j];
+    	          }
+    	  }
+          
+      }
+
+      // Update task queue 
+      if (start + CHUNK < npoints) {
+          start = args.global_i;
+          args.global_i = start + CHUNK;
+      } else {
+        break;
+      }
+    }//end of while
+
+      args.global_delta = args.global_delta + delta;
+  }
+
+	/*
+	 * ==========================================================================
+	 * === normal_exec
+	 * ==========================================================
+	 * ===================
+	 */
+	public float[][] normal_exec(int nthreads, float[][] feature, /*
+																 * in:
+																 * [npoints][
+																 * nfeatures]
+																 */
+	int nfeatures, int npoints, int nclusters, float threshold,
+			int[] membership, Random randomPtr, /* out: [npoints] */
+			GlobalArgs args) {
+		float delta;
+		float[][] clusters; /* out: [nclusters][nfeatures] */
+
+		/* Allocate space for returning variable clusters[] */
+		clusters = new float[nclusters][nfeatures];
+
+		/* Randomly pick cluster centers */
+		for (int i = 0; i < nclusters; i++) {
+			int n = (int) (randomPtr.random_generate() % npoints);
+			for (int j = 0; j < nfeatures; j++) {
+				clusters[i][j] = feature[n][j];
+			}
+		}
+
+		for (int i = 0; i < npoints; i++) {
+			membership[i] = -1;
+		}
+
+		/*
+		 * Need to initialize new_centers_len and new_centers[0] to all 0.
+		 * Allocate clusters on different cache lines to reduce false sharing.
+		 */
+		int[] new_centers_len = new int[nclusters];
+
+		float[][] new_centers = new float[nclusters][nfeatures];
+
+		int loop = 0;
+
+		long start = System.currentTimeMillis();
+		do {
+			delta = 0.0f;
+
+			args.feature = feature;
+			args.nfeatures = nfeatures;
+			args.npoints = npoints;
+			args.nclusters = nclusters;
+			args.membership = membership;
+			args.clusters = clusters;
+			args.new_centers_len = new_centers_len;
+			args.new_centers = new_centers;
+
+			args.global_i = nthreads * CHUNK;
+			args.global_delta = delta;
+
+			// Work in parallel with other threads
+			thread_work(args);
+
+			delta = args.global_delta;
+
+			/* Replace old cluster centers with new_centers */
+			for (int i = 0; i < nclusters; i++) {
+				for (int j = 0; j < nfeatures; j++) {
+					if (new_centers_len[i] > 0) {
+						clusters[i][j] = new_centers[i][j] / new_centers_len[i];
+					}
+					new_centers[i][j] = (float) 0.0; /* set back to 0 */
+				}
+				new_centers_len[i] = 0; /* set back to 0 */
+			}
+
+			delta /= npoints;
+
+		} while ((delta > threshold) && (loop++ < 500));
+		long stop = System.currentTimeMillis();
+		args.global_time += (stop - start);
+
+		return clusters;
+	}
+
+	/**
+	 * Work done by primary thread in parallel with other threads
+	 **/
+	void thread_work(GlobalArgs args) {
+		Normal.work(0, args); // threadId = 0 because primary thread
+	}
+}
+
+/*
+ * =============================================================================
+ * 
+ * End of normal.java
+ * 
+ * =============================================================================
+ */
+
+
diff --git a/Robust/src/Benchmarks/mlp/kmeans/README b/Robust/src/Benchmarks/mlp/kmeans/README
new file mode 100644
index 00000000..b25fc24d
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/README
@@ -0,0 +1,89 @@
+Introduction
+------------
+
+This benchmark was taken from NU-MineBench 2.0. Their technical report [3]
+provides a description of the algorithm:
+
+    K-means is a partition-based method [4] and is arguably the most
+    commonly used clustering technique. K-means represents a cluster by the
+    mean value of all objects contained in it. Given the user-provided
+    parameter k, the initial k cluster centers are randomly selected from
+    the database. Then, K-means assigns each object to its nearest cluster
+    center based on the similarity function. For example, for spatial
+    clustering, usually the Euclid distance is used to measure the closeness
+    of two objects. Once the assignments are completed, new centers are
+    found by finding the mean of all the objects in each cluster. This
+    process is repeated until two consecutive iterations generate the same
+    cluster assignment. The clusters produced by the K-means algorithm are
+    sometimes called "hard" clusters, since any data object either is or is
+    not a member of a particular cluster.
+
+
+Compiling and Running
+---------------------
+
+To build the application, simply run:
+
+    make 
+
+By default, this produces an executable named "KMeans.bin", which can then be
+run in the following manner:
+
+    ./KMeans.bin -m <max_clusters> \
+             -n <min_clusters> \
+             -t <threshold> \
+             -i <input_file_name> \
+             -nthreads <number of threads>
+
+To produce the data in [1], the following values were used:
+
+    low contention:  -m 40 -n 40 -t 0.05 -i inputs/random-n2048-d16-c16.txt
+    high contention: -m 15 -n 15 -t 0.05 -i inputs/random-n2048-d16-c16.txt
+
+For runs without a simulator, a larger input file, (more info below) can be used
+instead:
+
+    low contention:  -m 40 -n 40 -t 0.00001 -i inputs/random-n65536-d32-c16.txt
+    high contention: -m 15 -n 15 -t 0.00001 -i inputs/random-n65536-d32-c16.txt   
+
+
+Input Files
+-----------
+
+A section of the real image database distributed by Corel Corporation is used
+for K-means. This database consists of 17695 scenery pictures. Each picture is
+represented by two features: color and edge. The color feature is a vector of 9
+floating points while the edge feature is a vector of size 18. Both K-means
+implementations use Euclid distance as the similarity function and execute it
+for the two features separately. Since the clustering quality of K-means
+methods highly depends on the input parameter k, both K-means were executed
+with ten different k values ranging from 4 to 13.
+
+Smaller versions of "color", "edge", and "texture" were made by taking the
+first 100 lines of each file. The smaller inputs are called "color100",
+"edge100", and "texture100".
+
+The randomX_Y files were created by generating random data sets with X points
+of Y dimensions each. Values were selected from a uniform distribution.
+
+More input sets can be generated by using "inputs/generate.py". The script picks
+a given number of uniformly distributed random centers and then selects random
+points around those centers using a gaussian distribution. There are two 
+included input files from this script: small, random-r2048-d16-c16.txt; and
+large, random-r65536-d32-c16.txt. In the filename, "n" refers to the number of
+points, "d" the number of dimensions, and "c" the number of centers.
+
+
+References
+----------
+
+[1] C. Cao Minh, J. Chung, C. Kozyrakis, and K. Olukotun. STAMP: Stanford 
+    Transactional Applications for Multi-processing. In IISWC '08: Proceedings
+    of The IEEE International Symposium on Workload Characterization,
+    September 2008. 
+
+[2] J. Pisharath. NU-MineBench 2.0. Technical Report CUCIS-2005-08-01, 2005.
+
+[3] J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms.
+    Kluwer Academic Publishers, 1981
+
diff --git a/Robust/src/Benchmarks/mlp/kmeans/Random.java b/Robust/src/Benchmarks/mlp/kmeans/Random.java
new file mode 100644
index 00000000..611592b2
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/Random.java
@@ -0,0 +1,102 @@
+public class Random {
+  int[] mt; 
+  int mti;
+  int RANDOM_DEFAULT_SEED;
+  /* period parameter */
+  int N;
+  int M;
+  int MATRIX_A;
+  int UPPER_MASK;
+  int LOWER_MASK;
+  int[] mag01;
+
+  public Random() {
+    RANDOM_DEFAULT_SEED = 0;
+    N = 624;
+    M = 397;
+    mt = new int[N];
+    mti = N;
+    MATRIX_A = 0x9908b0df;   /* constant vector a */
+    UPPER_MASK = 0x80000000; /* most significant w-r bits */
+    LOWER_MASK = 0x7fffffff; /* least significant r bits */
+    mag01 = new int[2];
+    mag01[0] = 0x0;
+    mag01[1] = MATRIX_A;
+
+  }
+
+  public void random_alloc() {
+    init_genrand(this.RANDOM_DEFAULT_SEED);
+  }
+
+  /* initializes mt[N] with a seed */
+  public void init_genrand(int s) {
+    mt[0]= s & 0xFFFFFFFF;
+    for (int mti=1; mti<N; mti++) {
+     mt[mti] = (1812433253 * (mt[mti-1] ^ (mt[mti-1] >> 30)) + mti);
+      /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
+      /* In the previous versions, MSBs of the seed affect   */
+      /* only MSBs of the array mt[].                        */
+      /* 2002/01/09 modified by Makoto Matsumoto             */
+      mt[mti] &= 0xFFFFFFFF;
+      /* for >32 bit machines */
+    }
+    this.mti=mti;
+  }
+
+  public void random_seed(int seed) {
+    init_genrand(seed);
+  }
+
+  public int random_generate() {
+    return genrand_int32();
+  }
+
+  public int posrandom_generate() {
+    int r=genrand_int32();
+    if (r>0)
+      return r;
+    else 
+      return -r;
+  }
+
+  public int genrand_int32() {
+    int y;
+    int mti = this.mti;
+
+    /* mag01[x] = x * MATRIX_A  for x=0,1 */
+
+    if (mti >= 624) { /* generate N words at one time */
+      int kk;
+      int[] mt = this.mt;
+
+      if (mti == 624+1)   /* if init_genrand() has not been called, */
+        init_genrand(5489); /* a default initial seed is used */
+
+      for (kk=0;kk<(624-397);kk++) {
+        y = (mt[kk]&0x80000000)|(mt[kk+1]&0x7fffffff);
+        mt[kk] = mt[kk+397] ^ (y >> 1) ^ ((y & 0x1)==0 ? 0:0x9908b0df);
+      }
+      for (;kk<(624-1);kk++) {
+        y = (mt[kk]&0x80000000)|(mt[kk+1]&0x7fffffff);
+        mt[kk] = mt[kk+(397-624)] ^ (y >> 1) ^ ((y & 0x1)==0 ? 0:0x9908b0df);
+      }
+      y = (mt[624-1]&0x80000000)|(mt[0]&0x7fffffff);
+      mt[624-1] = mt[397-1] ^ (y >> 1) ^ ((y & 0x1)==0 ? 0:0x9908b0df);
+
+      mti = 0;
+    }
+
+    y = mt[mti++];
+
+    /* Tempering */
+    y ^= (y >> 11);
+    y ^= (y << 7) & 0x9d2c5680;
+    y ^= (y << 15) & 0xefc60000;
+    y ^= (y >> 18);
+
+    this.mti = mti;
+
+    return y;
+  }
+}
diff --git a/Robust/src/Benchmarks/mlp/kmeans/RandomExact.java b/Robust/src/Benchmarks/mlp/kmeans/RandomExact.java
new file mode 100644
index 00000000..1862c3b4
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/RandomExact.java
@@ -0,0 +1,87 @@
+public class Random {
+  long[] mt; 
+  int mti;
+  int RANDOM_DEFAULT_SEED;
+  /* period parameter */
+
+
+  public Random() {
+    RANDOM_DEFAULT_SEED = 0;
+    mt = new long[624];
+  }
+
+  public void random_alloc() {
+    init_genrand(this.RANDOM_DEFAULT_SEED);
+  }
+
+  /* initializes mt[N] with a seed */
+  public void init_genrand(int s) {
+    mt[0]= ((long)s) & 0xFFFFFFFFL;
+    for (int mti=1; mti<624; mti++) {
+      mt[mti] = (1812433253L * (mt[mti-1] ^ (mt[mti-1] >> 30)) + ((long)mti));
+      /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
+      /* In the previous versions, MSBs of the seed affect   */
+      /* only MSBs of the array mt[].                        */
+      /* 2002/01/09 modified by Makoto Matsumoto             */
+      mt[mti] &= 0xFFFFFFFFL;
+      /* for >32 bit machines */
+    }
+    this.mti=624;
+  }
+
+  public void random_seed(int seed) {
+    init_genrand(seed);
+  }
+
+  public long random_generate() {
+    long x= genrand_int32()&0xFFFFFFFFL;
+    return x;
+  }
+
+  public long posrandom_generate() {
+    long r=genrand_int32();
+    if (r>0)
+      return r;
+    else 
+      return -r;
+  }
+
+  public long genrand_int32() {
+    long y;
+    int mti = this.mti;
+    long[] mt = this.mt;
+
+    if (mti >= 624) { /* generate N words at one time */
+      int kk;
+
+      if (mti == 624+1) {  /* if init_genrand() has not been called, */
+        init_genrand(5489); /* a default initial seed is used */
+	mti=this.mti;
+      }
+      for (kk=0;kk<(624-397);kk++) {
+        y = (mt[kk]&0x80000000L)|(mt[kk+1]&0x7fffffffL);
+        mt[kk] = mt[kk+397] ^ (y >> 1) ^ ((y & 0x1)==0 ? 0L:0x9908b0dfL);
+      }
+      for (;kk<(624-1);kk++) {
+        y = (mt[kk]&0x80000000L)|(mt[kk+1]&0x7fffffffL);
+        mt[kk] = mt[kk+(397-624)] ^ (y >> 1) ^ ((y & 0x1)==0 ? 0L:0x9908b0dfL);
+      }
+      y = (mt[624-1]&0x80000000L)|(mt[0]&0x7fffffffL);
+      mt[624-1] = mt[397-1] ^ (y >> 1) ^ ((y & 0x1)==0 ? 0L:0x9908b0dfL);
+
+      mti = 0;
+    }
+
+    y = mt[mti++];
+
+    /* Tempering */
+    y ^= (y >> 11);
+    y ^= (y << 7) & 0x9d2c5680L;
+    y ^= (y << 15) & 0xefc60000L;
+    y ^= (y >> 18);
+
+    this.mti = mti;
+
+    return y;
+  }
+}
diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/color b/Robust/src/Benchmarks/mlp/kmeans/inputs/color
new file mode 100644
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/color100 b/Robust/src/Benchmarks/mlp/kmeans/inputs/color100
new file mode 100644
index 00000000..8359e623
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/color100
@@ -0,0 +1,100 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/edge b/Robust/src/Benchmarks/mlp/kmeans/inputs/edge
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/edge100 b/Robust/src/Benchmarks/mlp/kmeans/inputs/edge100
new file mode 100644
index 00000000..db00b85a
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/edge100
@@ -0,0 +1,100 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/generate.py b/Robust/src/Benchmarks/mlp/kmeans/inputs/generate.py
new file mode 100755
index 00000000..05e13c15
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/generate.py
@@ -0,0 +1,41 @@
+#!/usr/bin/python
+
+import random
+import sys
+
+if len(sys.argv) != 4:
+    print "Usage: generate.py <rows> <dimensions> <centers>"
+    sys.exit(1)
+
+numRow    = int(sys.argv[1])
+numDim    = int(sys.argv[2])
+numCenter = int(sys.argv[3])
+
+random.seed(0)
+
+if 0:
+    # uniform random
+    rows = []
+    for row in range(1, numRow+1):
+        print row,
+        for dim in range(numDim):
+            print random.random(),
+        print
+
+else:
+    # clustered random using gaussian
+    centers = []
+    for i in range(numCenter):
+        center = []
+        for dim in range(numDim):
+            center.append(random.random())
+        centers.append(center)
+    sigma = (1. / numCenter) ** 3
+    for row in range(1, numRow+1):
+        center = random.choice(centers)
+        print row,
+        for dim in range(numDim):
+            noise = random.gauss(0, sigma)
+            print center[dim] + noise,
+        print
+    
diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n150-d16-c16.txt b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n150-d16-c16.txt
new file mode 100644
index 00000000..99d51ec7
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n150-d16-c16.txt
@@ -0,0 +1,50 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n16384-d24-c16.txt b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n16384-d24-c16.txt
new file mode 100644
index 00000000..a1dd6e2a
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n16384-d24-c16.txt
@@ -0,0 +1,16384 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n2048-d16-c16.txt b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n2048-d16-c16.txt
new file mode 100644
index 00000000..4c4b2729
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n2048-d16-c16.txt
@@ -0,0 +1,2048 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n65536-d32-c16.txt b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n65536-d32-c16.txt
new file mode 100644
index 00000000..265b85a1
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random-n65536-d32-c16.txt
@@ -0,0 +1,65536 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random10000_12 b/Robust/src/Benchmarks/mlp/kmeans/inputs/random10000_12
new file mode 100644
index 00000000..ba27a3a6
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random10000_12
@@ -0,0 +1,10000 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random1000_12 b/Robust/src/Benchmarks/mlp/kmeans/inputs/random1000_12
new file mode 100644
index 00000000..58aca29e
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random1000_12
@@ -0,0 +1,1000 @@
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diff --git a/Robust/src/Benchmarks/mlp/kmeans/inputs/random50000_12 b/Robust/src/Benchmarks/mlp/kmeans/inputs/random50000_12
new file mode 100644
index 00000000..3f73026a
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/inputs/random50000_12
@@ -0,0 +1,50000 @@
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+ 56 2.376105 -0.898104 0.096518 -0.662393 -1.237146 -0.745721 0.909492 0.471537 0.533096 2.011730 1.529467 0.427161 -0.899045 1.256211 0.080744 -0.517591 0.070967 1.297972 0.266473 0.391455 
+ 57 0.211019 0.015806 0.619342 -1.289867 0.877630 -0.033806 -0.535862 1.150728 0.264700 0.513775 2.815895 -0.587267 -0.644614 0.491430 -1.176864 -0.665339 -0.555585 -1.364635 -0.763229 -0.552596 
+ 58 -0.003386 -0.430652 -0.298277 -0.869822 0.169984 0.583459 -0.403817 0.604995 0.109699 -0.216121 1.836591 -1.443366 -0.840105 0.096569 -1.609723 -0.755277 -0.713547 -1.384738 -0.500479 -0.954838 
+ 59 -1.040320 0.439694 -0.195874 -2.351349 1.291514 0.029308 -1.901377 0.657407 0.370809 -1.283746 0.199808 -0.607037 -0.209333 -0.221814 -0.718174 -0.154860 -0.702556 -0.563854 0.469242 -0.826999 
+ 60 -1.138603 -0.332606 0.072274 -0.370163 0.422366 0.178406 -0.025102 0.846862 0.177322 -0.577129 0.155895 -2.104805 -1.998285 -1.562354 -2.261909 -2.122727 -2.062836 -2.191984 -1.728378 -1.997160 
+ 61 -1.525721 -0.201945 0.115888 -0.387581 0.407708 0.106271 -0.078244 0.898931 0.138827 -0.072757 -0.697539 -2.198838 -2.127295 -1.929356 -2.248714 -2.092686 -2.070513 -2.190726 -1.774783 -1.941106 
+ 62 -0.286667 -0.176714 0.906760 0.185587 -0.156124 1.059121 0.437411 1.018132 0.455076 0.149345 1.628113 0.191120 0.654525 0.168690 -0.364711 -0.138452 -0.696633 -0.334429 0.722140 -0.495519 
+ 63 -0.799516 -0.642038 -0.338524 -1.419695 -0.079393 -0.061783 -0.619605 0.540869 -0.064026 0.189634 0.141012 -1.641525 -1.811930 -1.284433 -1.845231 -1.848301 -1.330326 -1.853166 -1.488305 -1.245169 
+ 64 -0.164244 -0.510967 0.020578 -0.026919 0.148158 0.140025 -0.489431 0.653785 0.139867 -0.390001 -0.225678 -1.971837 -1.753022 -2.076701 -2.122441 -2.235508 -2.432292 -2.124001 -1.810907 -2.211462 
+ 65 -1.789286 -0.600770 -0.544377 -0.445788 0.528894 -0.161764 -0.101903 0.883808 0.027519 -0.031954 -0.433813 -1.549914 -1.602940 -1.267235 -1.923480 -1.674656 -1.651469 -2.089238 -1.548906 -1.860631 
+ 66 -1.626331 -0.460380 -0.018281 -0.323820 0.398775 0.064179 0.572697 0.883412 0.099293 -1.843896 0.292584 -2.111527 -2.128490 -1.874387 -2.193984 -2.146138 -1.193683 -2.199627 -1.704128 -1.289898 
+ 67 0.224593 0.198234 -0.000372 -1.041176 0.878327 -0.006287 0.230191 1.331373 0.149233 0.068844 -1.422651 -2.087480 -2.174797 -1.537062 -2.155176 -2.151389 -1.757715 -2.095422 -1.758448 -1.673692 
+ 68 -0.220218 -0.810282 -0.504776 0.762390 0.057407 -0.618587 0.357574 0.761309 -0.236710 -0.728505 1.245767 -1.750766 -1.091723 0.675813 -1.841357 -1.218856 1.234200 -1.751542 -1.152029 0.509396 
+ 69 0.436242 -0.569966 -0.064845 -0.263615 0.024820 -0.016725 -0.917893 0.560652 0.073292 0.217279 0.562914 -1.175954 -1.134940 -0.753830 -1.563280 -1.291108 -0.756507 -1.547182 -0.931489 -0.921780 
+ 70 0.225390 -0.869214 0.308107 1.346823 -0.040687 0.852709 0.938333 0.456976 0.473799 -0.245206 1.453877 -1.438613 -1.479004 0.749735 -1.622074 -1.346731 0.654964 -1.665566 -1.214869 -0.027052 
+ 71 -0.927793 -0.341309 0.012363 -0.898607 0.392334 0.295751 -0.811644 0.872262 0.113857 -0.342343 -0.109132 -1.876414 -1.925915 -1.175627 -2.105412 -2.076306 -1.852896 -2.134203 -1.722273 -1.878449 
+ 72 -0.101950 -0.402679 -0.069951 0.694730 0.343527 -0.016561 -0.677569 0.753922 0.264700 -0.701730 -0.285439 -2.083128 -2.144771 -0.594930 -2.211397 -2.123754 -0.885838 -2.136874 -1.723860 -1.301872 
+ 73 0.230707 -0.364231 -0.168200 -0.296050 0.230591 0.092278 0.525303 0.749378 0.142986 0.239899 0.708438 -2.081153 -2.204869 -1.000422 -2.274753 -2.250606 -1.185501 -2.199945 -1.816617 -1.216417 
+ 74 -0.356365 -0.266422 0.348517 -0.894180 0.553773 0.408107 -0.168557 0.858947 -0.143086 -0.202645 0.362176 -2.057106 -1.826055 0.631888 -1.990295 -1.557114 -0.017330 -1.688021 -1.066915 -0.509795 
+ 75 -0.762661 -0.153070 0.295419 -1.155174 0.503297 0.162782 -0.600016 0.960135 0.233491 -0.129611 1.932055 -2.264862 -2.132446 1.358301 -2.289726 -2.269711 0.166917 -2.202456 -1.769513 -0.519095 
+ 76 -1.948302 -0.018255 0.153831 -1.390385 0.555640 -0.079359 -0.924317 0.988738 0.095134 -0.924431 -0.691944 -2.203200 -2.090523 -1.032182 -2.100743 -1.941354 -1.358192 -2.142248 -1.660686 -1.615778 
+ 77 -0.343596 -0.225871 -0.119302 -1.204821 0.411373 -0.011463 -1.090345 0.720569 0.188759 -1.140663 2.064636 -1.686138 -1.945152 -1.162897 -2.029869 -2.168683 -1.822141 -1.993164 -1.777034 -2.185007 
+ 78 -0.832793 -0.684367 -0.319479 0.206403 0.273752 0.214595 -0.297729 0.843466 0.111778 -0.677447 1.148043 -0.415736 -1.472998 -1.062263 -1.220025 -1.690557 -1.700981 -1.234046 -1.417899 -1.904812 
+ 79 1.830103 -0.388805 0.022419 1.714119 0.429856 0.006731 1.003542 0.622051 0.168996 -0.049866 0.379817 -1.364500 -1.926452 -0.152628 -1.722494 -1.703478 -0.743151 -1.295143 -1.179192 -0.758912 
+ 80 0.126571 -0.105716 -0.284694 0.891413 0.500259 -0.258288 0.193645 0.581116 -0.093154 0.976172 1.606317 -2.051966 -1.754607 0.426816 -2.153126 -1.876449 -0.210334 -1.937118 -1.570633 -0.484350 
+ 81 -1.088345 -0.865198 -0.041835 -2.344579 0.317023 -0.137799 -1.166815 0.777817 0.354165 -0.365260 -0.091477 -0.648813 -1.824113 -1.679723 -1.199961 -1.799797 -1.998534 -1.427692 -1.449863 -1.926590 
+ 82 -0.270903 -0.810382 -0.382142 -0.194690 0.562004 0.599261 -0.691719 0.569606 0.131549 0.590993 0.009049 -0.620479 -1.328979 -0.606818 -1.257255 -1.623650 -0.804821 -1.140130 -1.402861 -0.908900 
+ 83 -0.850258 -0.602633 0.020359 -0.518551 -0.079830 -0.066957 -0.268899 0.431288 0.120104 0.230046 1.347123 -1.346863 -1.370851 0.300646 -1.650156 -1.747062 -0.497626 -1.679765 -1.576237 -0.805844 
+ 84 0.536629 -0.303206 -0.505791 -0.739967 0.291193 0.291156 1.062018 0.582089 0.266779 0.694214 2.483064 -1.188782 -1.035061 0.403824 -1.159645 -0.228691 0.293590 -0.737333 0.450444 0.306912 
+ 85 1.342773 0.520122 -0.031877 1.580322 1.003507 0.046211 -0.061808 1.133747 -0.504067 1.952602 1.478239 -1.605897 -1.426051 1.395116 -1.702479 -1.257042 -0.065329 -1.841511 -1.465821 -0.298876 
+ 86 -0.572567 -0.333477 -1.075551 -1.483258 0.017755 0.708603 -1.130711 0.543918 0.527889 -0.753913 1.545327 -1.402538 -1.501211 0.046155 -1.590261 -1.398428 -0.876630 -1.668993 -1.378894 -1.435444 
+ 87 0.415531 -0.498482 -0.168657 1.636829 0.251395 0.212051 -0.949394 0.795150 0.129461 -0.444208 0.353623 -1.686015 -1.192223 0.597956 -1.861882 -1.259780 -0.197021 -1.708002 -1.117394 -0.717851 
+ 88 -0.643726 0.603674 0.027875 0.096524 1.126050 0.509773 0.988591 1.162561 -0.380274 1.038077 1.254952 -1.633097 -1.419891 -0.307675 -1.673094 -1.104920 -1.009107 -1.179254 -0.641037 -0.893713 
+ 89 -1.115869 -0.208631 0.616966 -0.324001 0.415586 -0.078769 -0.401040 0.793390 0.306313 -0.670374 -0.703480 -1.494691 -1.651695 -1.887882 -1.903941 -1.757849 -2.135848 -1.862068 -1.353616 -2.324397 
+ 90 -1.851821 -0.714191 -0.241675 -0.813899 0.461462 0.215063 -0.929221 0.912642 0.304234 -0.730762 -0.436084 -0.312064 -1.078122 -1.803355 -0.725180 -1.394886 -2.438539 -0.846935 -0.827231 -2.327887 
+ 91 -0.050138 -0.023791 -0.045623 -2.679785 0.518075 0.059964 -0.038851 0.800341 -0.085876 -0.287866 -0.079816 -2.392282 -2.093678 -0.532959 -2.339500 -2.102886 -1.366315 -2.182192 -1.733799 -1.201357 
+ 92 -0.993442 0.020006 -2.034294 -1.771853 0.914428 -1.294175 -0.562463 1.009849 0.067045 -0.409218 1.899238 -1.285959 -0.329123 -0.532781 -1.490963 -0.660052 -1.169806 -1.664563 -0.732408 -1.428540 
+ 93 0.327583 0.052503 0.167617 -0.166335 -0.072799 -0.154850 0.363524 0.999233 0.037916 0.106289 1.083541 -1.440167 -1.875889 -1.171791 -1.333642 -1.857583 -1.585142 -0.818630 -1.526288 -1.717436 
+ 94 0.247057 -0.192296 0.248064 -1.709751 0.331467 0.108082 0.072536 0.695983 0.285511 0.111116 0.008246 -1.307643 -1.315444 0.202388 -1.591601 -1.655439 -1.606591 -1.695545 -1.319184 -1.269833 
+ 95 1.848337 -0.887133 -0.106177 -0.365198 0.089670 -1.094185 0.852677 0.136571 -0.310572 0.540730 1.286485 -0.747717 -0.702073 -0.792478 -0.960772 -0.264481 -0.967231 -0.742965 0.609066 -0.194544 
+ 96 -1.165236 -0.179404 0.042056 -1.992848 0.449644 0.085705 -0.640481 0.738298 0.015034 0.752630 0.428191 -2.470264 -2.446229 -0.281358 -2.369138 -2.316291 -0.690136 -2.140656 -1.816406 -1.008367 
+ 97 0.580804 -0.051999 -0.326052 -0.206430 0.211430 0.216546 0.471310 0.515169 0.073292 0.235606 0.456429 -1.541931 -0.543467 -0.041427 -1.625737 -1.147010 -0.191389 -1.623542 -1.078932 -0.757038 
+ 98 -0.339361 -0.285190 -0.115357 -0.829386 0.368818 0.072243 0.468288 0.641605 0.189798 0.642311 0.084970 -2.303774 -1.966107 -0.977320 -2.257955 -1.975434 -1.387092 -1.992590 -1.639384 -1.299902 
+ 99 0.002298 1.588245 0.566624 0.633916 0.430237 -0.851723 1.582427 0.731473 0.438432 2.188517 3.394017 -1.369776 -0.579382 -0.005835 -1.069874 -1.151101 -0.173282 -0.879401 -1.062454 -0.068774 
+100 -1.086808 0.060357 0.040425 -0.021625 0.420924 0.088867 0.208215 0.866273 0.079530 0.205993 -0.424357 -2.303639 -2.206430 -2.066659 -2.289263 -2.050902 -1.979545 -2.136083 -1.687001 -1.797159 
diff --git a/Robust/src/Benchmarks/mlp/kmeans/makefile b/Robust/src/Benchmarks/mlp/kmeans/makefile
new file mode 100644
index 00000000..77701003
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/makefile
@@ -0,0 +1,44 @@
+MAINCLASS=KMeans
+PROGRAM=test
+BUILDSCRIPT=~/eclipse/workspaces/irvine_sep09/Robust/src/buildscript
+SRC=${MAINCLASS}.java \
+	Random.java \
+	Cluster.java \
+    Normal.java \
+    Common.java \
+    GlobalArgs.java 
+#    \../../../ClassLibrary/JavaSTM/Barrier.java
+    
+
+#include ../common/Makefile.flags
+
+#include ../common/Makefile.builds
+
+#FLAGSSTATS=-mainclass ${MAINCLASS} -singleTM -optimize -debug -joptimize -32bit -abcclose -dcopts -transstats -arraypad -stmstats
+
+USEMLP=  -mlp 8 2 -mlpdebug  # use to turn mlp on and off and make sure rest of build not broken
+BSFLAGS=   -32bit -profile -nooptimize -debug -garbagestats -mainclass ${MAINCLASS} 
+OWNERSHIP= -ownership -ownallocdepth 1 -enable-assertions  -methodeffects -flatirusermethods -ownwritedots final -ownaliasfile aliases.txt
+
+default:
+	../../../buildscript -nojava $(USEMLP) $(BSFLAGS) $(OWNERSHIP) -o $(PROGRAM)  ${SRC}
+
+java:
+	../../../buildscript $(USEMLP) $(BSFLAGS) $(OWNERSHIP) -o $(PROGRAM) ${SRC}
+
+single:
+	../../../buildscript $(BSFLAGS) -o $(PROGRAM) ${SRC}
+	
+
+prep:
+
+clean:
+	rm *.bin
+	rm -fr tmpbuilddirectory
+	rm -f  *~
+	rm -f  *.dot
+	rm -f  *.png
+	rm -f  *.txt
+	rm -f  aliases.txt
+	rm -f  mlpReport*txt
+	rm -f  results*txt
diff --git a/Robust/src/Benchmarks/mlp/kmeans/run b/Robust/src/Benchmarks/mlp/kmeans/run
new file mode 100755
index 00000000..1c52a29a
--- /dev/null
+++ b/Robust/src/Benchmarks/mlp/kmeans/run
@@ -0,0 +1 @@
+./test.bin -m 15 -n 15 -t 0.00001 -i inputs/random-n16384-d24-c16.txt -nthreads 1
\ No newline at end of file
-- 
2.34.1