[jira] [Assigned] (SPARK-19368) Very bad performance in BlockMatrix.toIndexedRowMatrix()

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19368?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen reassigned SPARK-19368:
---------------------------------

    Assignee: Ohad Raviv

> Very bad performance in BlockMatrix.toIndexedRowMatrix()
> --------------------------------------------------------
>
>                 Key: SPARK-19368
>                 URL: https://issues.apache.org/jira/browse/SPARK-19368
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 2.0.0, 2.1.0
>            Reporter: Ohad Raviv
>            Assignee: Ohad Raviv
>            Priority: Minor
>         Attachments: profiler snapshot.png
>
>
> In SPARK-12869, this function was optimized for the case of dense matrices using Breeze. However, I have a case with very very sparse matrices which suffers a great deal from this optimization. A process we have that took about 20 mins now takes about 6.5 hours.
> Here is a sample code to see the difference:
> {quote}
>     val n = 40000
>     val density = 0.0002
>     val rnd = new Random(123)
>     val rndEntryList = (for (i <- 0 until (n*n*density).toInt) yield (rnd.nextInt\(n\), rnd.nextInt\(n\), rnd.nextDouble()))
>                   .groupBy(t => (t._1,t._2)).map\(t => t._2.last).map\{ case (i,j,d) => (i,(j,d)) }.toSeq
>     val entries: RDD\[(Int, (Int, Double))] = sc.parallelize(rndEntryList, 10)
>     val indexedRows = entries.groupByKey().map(e => IndexedRow(e._1, Vectors.sparse(n, e._2.toSeq)))
>     val mat = new IndexedRowMatrix(indexedRows, nRows = n, nCols = n)
>     val t1 = System.nanoTime()
>     println(mat.toBlockMatrix(10000,10000).toCoordinateMatrix().toIndexedRowMatrix().rows.map(_.vector.numActives).sum())
>     val t2 = System.nanoTime()
>     println("took: " + (t2 - t1) / 1000 / 1000 + " ms")
>     println("============================================================")
>     println(mat.toBlockMatrix(10000,10000).toIndexedRowMatrix().rows.map(_.vector.numActives).sum())
>     val t3 = System.nanoTime()
>     println("took: " + (t3 - t2) / 1000 / 1000 + " ms")
>     println("============================================================")
> {quote}
> I get:
> {quote}
> took: 9404 ms
> ============================================================
> took: 57350 ms
> ============================================================
> {quote}
> Looking at it a little with a profiler, I see that the problem is with the SliceVector.update() and SparseVector.apply.
> I currently work-around this by doing:
> {quote}
> blockMatrix.toCoordinateMatrix().toIndexedRowMatrix()
> {quote}
> like it was in version 1.6.



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