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From "Glenn Weidner (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-1792) Performance issue sparse-dense matrix multiply
Date Sat, 09 Sep 2017 02:23:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-1792?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Glenn Weidner updated SYSTEMML-1792:
------------------------------------
    Fix Version/s:     (was: SystemML 1.0)
                   SystemML 0.15

> Performance issue sparse-dense matrix multiply
> ----------------------------------------------
>
>                 Key: SYSTEMML-1792
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1792
>             Project: SystemML
>          Issue Type: Bug
>            Reporter: Matthias Boehm
>            Assignee: Matthias Boehm
>             Fix For: SystemML 0.15
>
>
> Our sparse-dense matrix multiply is already cache conscious but used very small block
static block sizes, which were optimized for moderate sparsity. However, for cases with very
sparse matrices (and skinny right hand size matrices), the small block sizes add substantial
overhead of more than an order of magnitude. This task aims to make these block sizes adaptive,
consistent with our cache-conscious implementations of sparsity exploiting matrix multiply
operators such as wsloss.



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