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https://issues.apache.org/jira/browse/MAHOUT-1837?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15262827#comment-15262827
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Andrew Palumbo commented on MAHOUT-1837:
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The first fix is trivial (that's the actual fix in the description) and does
not actually check the data for sparsity (it does help in the dense/dense case
which is currently being returned as sparse). Also the combiner above:
{{comb}} is the result of the in-core {{mmul}} class which is where we would
actully check for densified data. Since they're tightly coupled, it seemed to
make sense to knock them out all at once. Take a look at
https://github.com/apache/mahout/pull/228.
Maybe the {{blockify}} part belongs in another issue though. I have not looked
into that yet.
> 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
> Fix For: 0.12.1
>
>
> 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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