[jira] [Updated] (MAHOUT-1837) Sparse/Dense Matrix analysis for Matrix Multiplication

["Andrew Palumbo (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-1837?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Andrew Palumbo updated MAHOUT-1837:
-----------------------------------
    Attachment: compareDensityTest.ods

Spreadsheet of time comparisons for density calculation in {{MMul}} class vs. {{MatrixFlavor}}.

> 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: 0.12.1
>
>         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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