[SPARK-4708][MLLib] Make k-mean runs two/three times faster with dense/sparse sample

[dbtsai]

Note that the usage of breezeSquaredDistance in
org.apache.spark.mllib.util.MLUtils.fastSquaredDistance
is in the critical path, and breezeSquaredDistance is slow.
We should replace it with our own implementation.

Here is the benchmark against mnist8m dataset.

Before
DenseVector: 70.04secs
SparseVector: 59.05secs

With this PR
DenseVector: 30.58secs
SparseVector: 21.14secs

[mengxr]

we can use BLAS.axpy here.

[SparkQA]

Test build #24065 has started for PR 3565 at commit b185a77.

• This patch merges cleanly.

[SparkQA]

Test build #24065 has finished for PR 3565 at commit b185a77.

• This patch fails Scala style tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class VectorWithNorm(val vector: Vector, val norm: Double) extends Serializable

[AmplabJenkins]

Test FAILed.
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[dbtsai]

Calling BLAS will add very small extra overhead. The benchmark will now be

DenseVector: 33.19secs
SparseVector: 22.05secs

[SparkQA]

Test build #24067 has started for PR 3565 at commit de24662.

• This patch merges cleanly.

[SparkQA]

Test build #24068 has started for PR 3565 at commit 08bc068.

• This patch merges cleanly.

[SparkQA]

Test build #24067 has finished for PR 3565 at commit de24662.

• This patch passes all tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class VectorWithNorm(val vector: Vector, val norm: Double) extends Serializable

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Test PASSed.
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Test PASSed.

[SparkQA]

Test build #24068 has finished for PR 3565 at commit 08bc068.

• This patch passes all tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class VectorWithNorm(val vector: Vector, val norm: Double) extends Serializable

[AmplabJenkins]

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
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Test PASSed.

[mengxr]

LGTM. Merged into master and branch-1.2. Thanks!