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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-1837) Sparse/Dense Matrix analysis for Matrix Multiplication
Date Sat, 27 Aug 2016 16:21:20 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-1837?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15441848#comment-15441848
] 

ASF GitHub Bot commented on MAHOUT-1837:
----------------------------------------

Github user andrewpalumbo commented on a diff in the pull request:

    https://github.com/apache/mahout/pull/252#discussion_r76518811
  
    --- Diff: spark/src/main/scala/org/apache/mahout/sparkbindings/drm/package.scala ---
    @@ -60,26 +60,22 @@ package object drm {
             val keys = data.map(t => t._1).toArray[K]
             val vectors = data.map(t => t._2).toArray
     
    -        // create the block by default as dense.
    -        // would probably be better to sample a subset of these
    -        // vectors first before creating the entire matrix.
    -        // so that we don't have the overhead of creating a full second matrix in
    -        // the case that the matrix is not dense.
    -        val block = new DenseMatrix(vectors.length, blockncol)
    -        var row = 0
    -        while (row < vectors.length) {
    -          block(row, ::) := vectors(row)
    -          row += 1
    -        }
    +        // create the block by default as Sparse.
    +        val block = new SparseRowMatrix(vectors.length, blockncol, vectors, true, false)
     
    -        // Test the density of the data. If the matrix does not meet the
    -        // requirements for density, convert the Vectors to a sparse Matrix.
    +        // Test the density of the data. If the matrix does meets the
    +        // requirements for density, convert the Vectors to a DenseMatrix.
             val resBlock = if (densityAnalysis(block)) {
    -          block
    +          val dBlock = new DenseMatrix(vectors.length, blockncol)
    +          var row = 0
    +          while (row < vectors.length) {
    +            dBlock(row, ::) := vectors(row)
    +            row += 1
    +          }
    +          dBlock
             } else {
    -          new SparseRowMatrix(vectors.length, blockncol, vectors, true, false)
    --- End diff --
    
    thx.


> Sparse/Dense Matrix analysis for Matrix Multiplication
> ------------------------------------------------------
>
>                 Key: MAHOUT-1837
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1837
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Math
>    Affects Versions: 0.12.0
>            Reporter: Andrew Palumbo
>            Assignee: Andrew Palumbo
>             Fix For: 0.13.0
>
>         Attachments: compareDensityTest.ods
>
>
> In matrix multiplication, Sparse Matrices can easily turn dense and bloat memory,  one
fully dense column and one fully dense row can cause a sparse %*% sparse operation have a
dense result.  
> There are two issues here one with a quick Fix and one a bit more involved:
>    #  in {{ABt.Scala}} use check the `MatrixFlavor` of the combiner and use the flavor
of the Block as the resulting Sparse or Dense matrix type:
> {code}
> val comb = if (block.getFlavor == MatrixFlavor.SPARSELIKE) {
>               new SparseMatrix(prodNCol, block.nrow).t
>             } else {
>               new DenseMatrix(prodNCol, block.nrow).t
>             }
> {code}
>  a simlar check needs to be made in the {{blockify}} transformation.
>  
>    #  More importantly, and more involved is to do an actual analysis of the resulting
matrix data in the in-core {{mmul}} class and use a matrix of the appropriate Structure as
a result. 



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