diff --git a/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala b/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
index baf7291474..0ba39d8d80 100644
--- a/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
+++ b/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
@@ -381,12 +381,13 @@ object GraphImpl {
   }
 
 
+  /**
+   * @todo(crankshaw) how does this effect load balancing?
+   */
   protected def canonicalEdgePartitionFunction2D(srcOrig: Vid, dstOrig: Vid, 
     numParts: Pid, ceilSqrtNumParts: Pid): Pid = {
     val mixingPrime: Vid = 1125899906842597L 
-
     // Partitions by canonical edge direction
-    // @todo(crankshaw) evaluate the cases 
     val src = math.min(srcOrig, dstOrig)
     val dst = math.max(srcOrig, dstOrig)
     val col: Pid = ((math.abs(src) * mixingPrime) % ceilSqrtNumParts).toInt
@@ -411,7 +412,8 @@ 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 = edgePartitionFunction2D(e.src, e.dst, numPartitions, ceilSqrt)
+        val part: Pid = canonicalEdgePartitionFunction2D(e.src, e.dst, numPartitions, ceilSqrt)
 
         // Should we be using 3-tuple or an optimized class
         (part, (e.src, e.dst, e.data))