Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/3435#issuecomment-64304769
@mengxr
Without the local reference copy of `factor` and `shift` arrays, the
runtime is almost three time slower.
DenseVector withMean and withStd: 18.15secs
DenseVector withMean and withoutStd: 18.05secs
DenseVector withoutMean and withStd: 18.54secs
SparseVector withoutMean and withStd: 2.01secs
The following code,
```scala
while (i < size) {
values(i) = (values(i) - shift(i)) * factor(i)
i += 1
}
```
will generate the bytecode
```
L13
LINENUMBER 106 L13
FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel
org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector
org/apache/spark/mllib/linalg/DenseVector T [D I I] []
ILOAD 7
ILOAD 6
IF_ICMPGE L14
L15
LINENUMBER 107 L15
ALOAD 5
ILOAD 7
ALOAD 5
ILOAD 7
DALOAD
ALOAD 0
INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift
()[D
ILOAD 7
DALOAD
DSUB
ALOAD 0
INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor
()[D
ILOAD 7
DALOAD
DMUL
DASTORE
L16
LINENUMBER 108 L16
ILOAD 7
ICONST_1
IADD
ISTORE 7
GOTO L13
```
, while with the local reference of the `shift` and `factor` arrays, the
bytecode will be
```
L14
LINENUMBER 107 L14
ALOAD 0
INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor
()[D
ASTORE 9
L15
LINENUMBER 108 L15
FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel
org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector
org/apache/spark/mllib/linalg/DenseVector T [D I I [D] []
ILOAD 8
ILOAD 7
IF_ICMPGE L16
L17
LINENUMBER 109 L17
ALOAD 6
ILOAD 8
ALOAD 6
ILOAD 8
DALOAD
ALOAD 2
ILOAD 8
DALOAD
DSUB
ALOAD 9
ILOAD 8
DALOAD
DMUL
DASTORE
L18
LINENUMBER 110 L18
ILOAD 8
ICONST_1
IADD
ISTORE 8
GOTO L15
```
You can see that with local reference, the both of the arrays will be in
the stack, so JVM can access the value without calling `INVOKESPECIAL`.
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