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https://issues.apache.org/jira/browse/SYSTEMML-913?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Matthias Boehm updated SYSTEMML-913:
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Description: So far, we compute matrix-vector multiplication with simple
row-wise dot products. This works very well for the common case of tall&skinny
matrices, where the right-hand-side vector is very small. However, for
scenarios with many features and hence a tall rhs vector, this approach suffers
from cache unfriendly behavior. Accordingly, this task tracks the introduction
of a dedicated cache-conscious matrix-vector multiplication for both sparse and
dense matrices. (was: So far, we compute matrix-vector multiplication with
simple row-wise dot products. This works very well for the common case of
tall&skinny matrices, where the right-hand-side vector is very small. However,
for scenarios with many features and hence a tall rhs vector, this approach
suffers from cache unfriendly behavior. This tasks tracks the dedicated
handling of cache-conscious matrix-vector multiplication for both sparse and
dense matrices. )
> Performance matrix-vector multiplication w/ tall rhs vector
> -----------------------------------------------------------
>
> Key: SYSTEMML-913
> URL: https://issues.apache.org/jira/browse/SYSTEMML-913
> Project: SystemML
> Issue Type: Task
> Reporter: Matthias Boehm
>
> So far, we compute matrix-vector multiplication with simple row-wise dot
> products. This works very well for the common case of tall&skinny matrices,
> where the right-hand-side vector is very small. However, for scenarios with
> many features and hence a tall rhs vector, this approach suffers from cache
> unfriendly behavior. Accordingly, this task tracks the introduction of a
> dedicated cache-conscious matrix-vector multiplication for both sparse and
> dense matrices.
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