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From "Matthias Boehm (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SYSTEMML-1752) Cache-conscious mmchain matrix multiply for wide matrices
Date Sun, 09 Jul 2017 06:26:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-1752?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Matthias Boehm resolved SYSTEMML-1752.
--------------------------------------
       Resolution: Done
         Assignee: Matthias Boehm
    Fix Version/s: SystemML 1.0

> Cache-conscious mmchain matrix multiply for wide matrices
> ---------------------------------------------------------
>
>                 Key: SYSTEMML-1752
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1752
>             Project: SystemML
>          Issue Type: Task
>            Reporter: Matthias Boehm
>            Assignee: Matthias Boehm
>             Fix For: SystemML 1.0
>
>
> The fused mmchain matrix multiply for patterns such as {{t(X) %*% (w * (X %*% v))}} uses
row-wise {{dotProduct}} and {{vectMultAdd}} operations, which works very well for the common
case of tall&skinny matrices where individual rows fit into L1 cache. However, for graph
and text scenarios with wide matrices this leads to cache trashing on the input and output
vectors.
> This task aims to generalize these dense and sparse operations to perform the computation
in a cache-conscious manner when necessary, by accessing fragments of the input and output
vector for groups of rows. For dense this is trivial to realize while for sparse it requires
a careful determination of the block sizes according to the input sparsity. 



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