diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 1c4ae0b0ccafd..66f1a8066525b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -509,15 +509,24 @@ class RowMatrix(
    */
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
+    val k = B.numCols
     require(n == B.numRows, s"Dimension mismatch: $n vs ${B.numRows}")
 
     require(B.isInstanceOf[DenseMatrix],
       s"Only support dense matrix at this time but found ${B.getClass.getName}.")
 
-    val Bb = rows.context.broadcast(B)
+    val Bb = rows.context.broadcast(B.toBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
     val AB = rows.mapPartitions({ iter =>
-      val Bi = Bb.value.toBreeze.asInstanceOf[BDM[Double]]
-      iter.map(v => Vectors.fromBreeze(Bi.t * v.toBreeze))
+      val Bi = Bb.value
+      iter.map(row => {
+        val v = BDV.zeros[Double](k)
+        var i = 0
+        while (i < k) {
+          v(i) = row.toBreeze.dot(new BDV(Bi, i * n, 1, n))
+          i += 1
+        }
+        Vectors.fromBreeze(v)
+      })
     }, preservesPartitioning = true)
 
     new RowMatrix(AB, nRows, B.numCols)