Updated code so benchmarks actually run.

conf/core-site.xml

+<?xml version="1.0"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+
+<!-- Put site-specific property overrides in this file. -->
+
+<configuration>
+
+  <property>
+    <name>hadoop.tmp.dir</name>
+    <value>/mnt/ephemeral-hdfs</value>
+  </property>
+
+  <property>
+    <name>fs.default.name</name>
+    <value>hdfs://ec2-50-17-7-68.compute-1.amazonaws.com:9000</value>
+  </property>
+
+  <property>
+    <name>io.file.buffer.size</name>
+    <value>65536</value>
+  </property>
+
+  <property>
+    <name>dfs.client.read.shortcircuit</name>
+    <value>false</value>
+  </property>
+
+  <property>
+    <name>dfs.client.read.shortcircuit.skip.checksum</name>
+    <value>false</value>
+  </property>
+
+  <property>
+    <name>dfs.domain.socket.path</name>
+    <value>/var/run/hadoop-hdfs/dn._PORT</value>
+  </property>
+
+  <property>
+    <name>dfs.client.file-block-storage-locations.timeout</name>
+    <value>3000</value>
+  </property>
+
+</configuration>

conf/slaves

-# A Spark Worker will be started on each of the machines listed below.
-localhost
\ No newline at end of file
+ec2-23-20-12-62.compute-1.amazonaws.com
+ec2-54-205-173-19.compute-1.amazonaws.com
+ec2-54-225-4-124.compute-1.amazonaws.com
+ec2-23-22-209-112.compute-1.amazonaws.com
+ec2-50-16-69-88.compute-1.amazonaws.com
+ec2-54-205-163-126.compute-1.amazonaws.com
+ec2-54-242-235-95.compute-1.amazonaws.com
+ec2-54-211-169-232.compute-1.amazonaws.com
+ec2-54-237-31-30.compute-1.amazonaws.com
+ec2-54-235-15-124.compute-1.amazonaws.com

conf/spark-env.sh.template

 #!/usr/bin/env bash
 
 # This file contains environment variables required to run Spark. Copy it as
-# spark-env.sh and edit that to configure Spark for your site.
-#
-# The following variables can be set in this file:
-# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
+# spark-env.sh and edit that to configure Spark for your site. At a minimum,
+# the following two variables should be set:
+# - SCALA_HOME, to point to your Scala installation, or SCALA_LIBRARY_PATH to
+#   point to the directory for Scala library JARs (if you install Scala as a
+#   Debian or RPM package, these are in a separate path, often /usr/share/java)
 # - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
-# - SPARK_JAVA_OPTS, to set node-specific JVM options for Spark. Note that
-#   we recommend setting app-wide options in the application's driver program.
-#     Examples of node-specific options : -Dspark.local.dir, GC options
-#     Examples of app-wide options : -Dspark.serializer
 #
-# If using the standalone deploy mode, you can also set variables for it here:
-# - SPARK_MASTER_IP, to bind the master to a different IP address or hostname
+# If using the standalone deploy mode, you can also set variables for it:
+# - SPARK_MASTER_IP, to bind the master to a different IP address
 # - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports
 # - SPARK_WORKER_CORES, to set the number of cores to use on this machine
 # - SPARK_WORKER_MEMORY, to set how much memory to use (e.g. 1000m, 2g)
 # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT
-# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
+# - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes
+#   to be spawned on every slave machine
 

core/src/main/scala/org/apache/spark/SparkEnv.scala

@@ -156,6 +156,7 @@ object SparkEnv extends Logging {
 
     val serializer = serializerManager.setDefault(
       System.getProperty("spark.serializer", "org.apache.spark.serializer.JavaSerializer"))
+    logInfo("spark.serializer is " + System.getProperty("spark.serializer"))
 
     val closureSerializer = serializerManager.get(
       System.getProperty("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer"))

core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala

@@ -61,14 +61,10 @@ class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging
     kryo.register(classOf[HttpBroadcast[_]], new KryoJavaSerializer())
 
     // Allow the user to register their own classes by setting spark.kryo.registrator
-    try {
-      Option(System.getProperty("spark.kryo.registrator")).foreach { regCls =>
-        logDebug("Running user registrator: " + regCls)
-        val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator]
-        reg.registerClasses(kryo)
-      }
-    } catch {
-      case _: Exception => println("Failed to register spark.kryo.registrator")
+    Option(System.getProperty("spark.kryo.registrator")).foreach { regCls =>
+      logDebug("Running user registrator: " + regCls)
+      val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator]
+      reg.registerClasses(kryo)
     }
 
     kryo.setClassLoader(classLoader)
@@ -116,7 +112,7 @@ class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends Deser
   }
 }
 
-private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance {
+private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance with Logging {
   val kryo = ks.newKryo()
   val output = ks.newKryoOutput()
   val input = ks.newKryoInput()

core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala

@@ -25,7 +25,7 @@ import java.util.concurrent.ConcurrentHashMap
  * instance of the serializer object has been created, the get method returns that instead of
  * creating a new one.
  */
-private[spark] class SerializerManager {
+private[spark] class SerializerManager extends org.apache.spark.Logging {
 
   private val serializers = new ConcurrentHashMap[String, Serializer]
   private var _default: Serializer = _

graph/src/main/scala/org/apache/spark/graph/GraphKryoRegistrator.scala

@@ -4,7 +4,7 @@ import com.esotericsoftware.kryo.Kryo
 
 import org.apache.spark.graph.impl.MessageToPartition
 import org.apache.spark.serializer.KryoRegistrator
-
+import org.apache.spark.graph.impl._
 
 class GraphKryoRegistrator extends KryoRegistrator {
 
@@ -13,6 +13,8 @@ class GraphKryoRegistrator extends KryoRegistrator {
     kryo.register(classOf[Edge[Object]])
     kryo.register(classOf[MutableTuple2[Object, Object]])
     kryo.register(classOf[MessageToPartition[Object]])
+    kryo.register(classOf[(Vid, Object)])
+    kryo.register(classOf[EdgePartition[Object]])
 
     // This avoids a large number of hash table lookups.
     kryo.setReferences(false)

graph/src/main/scala/org/apache/spark/graph/GraphLoader.scala

@@ -20,7 +20,7 @@ object GraphLoader {
     : GraphImpl[Int, ED] = {
 
     // Parse the edge data table
-    val edges = sc.textFile(path).flatMap { line =>
+    val edges = sc.textFile(path, minEdgePartitions).flatMap { line =>
       if (!line.isEmpty && line(0) != '#') {
         val lineArray = line.split("\\s+")
         if(lineArray.length < 2) {

graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala

@@ -516,7 +516,7 @@ object GraphImpl {
       .map { e =>
         // Random partitioning based on the source vertex id.
         // val part: Pid = edgePartitionFunction1D(e.src, e.dst, numPartitions)
-        val part: Pid = edgePartitionFunction2D(e.src, e.dst, numPartitions, ceilSqrt)
+        val part: Pid = randomVertexCut(e.src, e.dst, numPartitions)
         //val part: Pid = canonicalEdgePartitionFunction2D(e.src, e.dst, numPartitions, ceilSqrt)
 
         // Should we be using 3-tuple or an optimized class

graph/src/main/scala/org/apache/spark/graph/util/GraphGenerators.scala

@@ -63,9 +63,6 @@ object GraphGenerators {
 
   }
 
-  // For now just writes graph to a file. Eventually
-  // it will return a spark.graph.Graph
-
 
   // Right now it just generates a bunch of edges where
   // the edge data is the weight (default 1)