Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/14937
@srowen Yes, I'm working on this. You can see the performance test result
in the PR description. We can found that the optimization k-means can get
performance improvements about 2 ~ 4 times by using native BLAS level 3
matrix-matrix multiplications for dense input. However, we saw performance
degradation for sparse input. For example, the new implementation spent almost
twice time as much as the old one when training k-means model on the famous
mnist data set.
In the view of the current performance test result, I think we should only
make this optimization for dense input and let sparse input still run the old
code.
I have sent the performance test result to @mengxr and waiting for his
opinion. I'm also appreciate your thoughts and suggestions. Thanks!
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