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

["Ohad Raviv (JIRA)" <ji...@apache.org>]

Ohad Raviv created SPARK-19368:
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             Summary: Very bad performance in BlockMatrix.toIndexedRowMatrix()
                 Key: SPARK-19368
                 URL: https://issues.apache.org/jira/browse/SPARK-19368
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 2.1.0, 2.0.0
            Reporter: Ohad Raviv


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:
BlockMatrix.toCoordinateMatrix().toIndexedRowMatrix()
like it was in the previous version.






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