Matthias Boehm closed SYSTEMML-1752.

> 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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