[GitHub] [spark] zhengruifeng edited a comment on issue #28229: [SPARK-31454][ML] An optimized K-Means based on DenseMatrix and GEMM

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zhengruifeng edited a comment on issue #28229:
URL: https://github.com/apache/spark/pull/28229#issuecomment-616919554


   @srowen Thanks for pinging me
   
   @xwu99 Could you please provide some performance results of your PR?
   
   I had [similar attempts](https://github.com/apache/spark/compare/master...zhengruifeng:blockify_km?expand=1) to optimize KMeans based on high level BLAS.
   I also blockfied vectors into blocks, and use BLAS.gemm to find best costs. But I found that:
   1, it will cause performance regression when input dataset is sparse, (I notice that you add `spark.ml.kmeans.matrixImplementation.rowsPerMatrix`, I am not sure whether we should have two implementations);
   2, when input dataset is dense, I found no performace gain when `distanceMeasure = EUCLIDEAN`; while `distanceMeasure = EUCLIDEAN`, about 10% ~ 20% speedup can be obtained;
   3, Native BLAS (Open-BLAS) did not help too much, if single-thread is used (which is suggested [in SPARK](https://spark.apache.org/docs/latest/ml-guide.html#dependencies));
   
   
   Then I swith to another optimization approach based on [triangle-inequality](https://github.com/apache/spark/pull/27758), it works on both dense and sparse dataset, and will gain about 10%~30% when `numFeatures` and/or `k` are large.
   


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