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From dbtsai <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4431][MLlib] Implement efficient active...
Date Wed, 19 Nov 2014 23:00:05 GMT
Github user dbtsai commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3288#discussion_r20615000
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala ---
    @@ -76,6 +76,22 @@ sealed trait Vector extends Serializable {
       def copy: Vector = {
         throw new NotImplementedError(s"copy is not implemented for ${this.getClass}.")
       }
    +
    +  /**
    +   * It will return the iterator for the active elements of dense and sparse vector as
    +   * (index, value) pair. Note that foreach method can be overridden for better performance
    +   * in different vector implementation.
    +   *
    +   * @param skippingZeros Skipping zero elements explicitly if true. It will be useful
when we
    +   *                      iterator through dense vector having lots of zero elements
which
    +   *                      we want to skip. Default is false.
    +   * @return Iterator[(Int, Double)] where the first element in the tuple is the index,
    +   *         and the second element is the corresponding value.
    +   */
    +  private[spark] def activeIterator(skippingZeros: Boolean): Iterator[(Int, Double)]
    --- End diff --
    
    You are right; the `Tuple2[Int, Double]` is specialized, and I mistakenly interpreted
the bytecode. 
    For the flowing scala code,
    ```scala
    def foreach[@specialized(Unit) U](f: ((Int, Double)) => U) {
      var i = 0
      val localValuesSize = values.size
      val localValues = values
      while (i < localValuesSize) {
        f(i, localValues(i))
        i += 1
      }
    }
    ```
    the generated bytecode will be
    ```
      public foreach(Lscala/Function1;)V
       L0
        LINENUMBER 296 L0
        ICONST_0
        ISTORE 2
       L1
        LINENUMBER 297 L1
        GETSTATIC scala/Predef$.MODULE$ : Lscala/Predef$;
        ALOAD 0
        INVOKEVIRTUAL org/apache/spark/mllib/linalg/DenseVector.values ()[D
        INVOKEVIRTUAL scala/Predef$.doubleArrayOps ([D)Lscala/collection/mutable/ArrayOps;
        INVOKEINTERFACE scala/collection/mutable/ArrayOps.size ()I
        ISTORE 3
       L2
        LINENUMBER 298 L2
        ALOAD 0
        INVOKEVIRTUAL org/apache/spark/mllib/linalg/DenseVector.values ()[D
        ASTORE 4
       L3
        LINENUMBER 299 L3
       FRAME APPEND [I I [D]
        ILOAD 2
        ILOAD 3
        IF_ICMPGE L4
       L5
        LINENUMBER 300 L5
        ALOAD 1
        NEW scala/Tuple2$mcID$sp
        DUP
        ILOAD 2
        ALOAD 4
        ILOAD 2
        DALOAD
        INVOKESPECIAL scala/Tuple2$mcID$sp.<init> (ID)V
        INVOKEINTERFACE scala/Function1.apply (Ljava/lang/Object;)Ljava/lang/Object;
        POP
       L6
        LINENUMBER 301 L6
        ILOAD 2
        ICONST_1
        IADD
        ISTORE 2
        GOTO L3
    ```
    
    However, 
    ```
        INVOKESPECIAL scala/Tuple2$mcID$sp.<init> (ID)V
        INVOKEINTERFACE scala/Function1.apply (Ljava/lang/Object;)Ljava/lang/Object;
    ```
    is expensive, so that's why checking zero in the anonymous function will slow down the
whole thing. 
    
    I agree with you, the iterator is slow by nature, and we are only interested in foreach
implementation. I'll remove the iterator, and just have foreach method in vector.


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