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

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

Andrew Palumbo created MAHOUT-1837:
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             Summary: 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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