[SPARK-4129][MLlib] Performance tuning in MultivariateOnlineSummarizer

In MultivariateOnlineSummarizer, breeze's activeIterator is used
to loop through the nonZero elements in the vector. However,
activeIterator doesn't perform well due to lots of overhead.
In this PR, native while loop is used for both DenseVector and SparseVector.

The benchmark result with 20 executors using mnist8m dataset:
Before:
DenseVector: 48.2 seconds
SparseVector: 16.3 seconds

After:
DenseVector: 17.8 seconds
SparseVector: 11.2 seconds

Since MultivariateOnlineSummarizer is used in several places,
the overall performance gain in mllib library will be significant with this PR.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #2992 from dbtsai/SPARK-4129 and squashes the following commits:

b99db6c [DB Tsai] fixed java.lang.ArrayIndexOutOfBoundsException
2b5e882 [DB Tsai] small refactoring
ebe3e74 [DB Tsai] First commit

mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala

@@ -20,7 +20,7 @@ package org.apache.spark.mllib.stat
 import breeze.linalg.{DenseVector => BDV}
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors, Vector}
 
 /**
  * :: DeveloperApi ::
@@ -72,9 +72,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     require(n == sample.size, s"Dimensions mismatch when adding new sample." +
       s" Expecting $n but got ${sample.size}.")
 
-    sample.toBreeze.activeIterator.foreach {
-      case (_, 0.0) => // Skip explicit zero elements.
-      case (i, value) =>
+    @inline def update(i: Int, value: Double) = {
+      if (value != 0.0) {
         if (currMax(i) < value) {
           currMax(i) = value
         }
@@ -89,6 +88,24 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
         currL1(i) += math.abs(value)
 
         nnz(i) += 1.0
+      }
+    }
+
+    sample match {
+      case dv: DenseVector => {
+        var j = 0
+        while (j < dv.size) {
+          update(j, dv.values(j))
+          j += 1
+        }
+      }
+      case sv: SparseVector =>
+        var j = 0
+        while (j < sv.indices.size) {
+          update(sv.indices(j), sv.values(j))
+          j += 1
+        }
+      case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
     }
 
     totalCnt += 1