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https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Niketan Pansare updated SYSTEMML-1140:
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Description:
We have identified two performance bugs that frequently occurs in deep learning
script.
First, we repeatedly perform unnecessary conversion to sparse format. Also, the
operations such as matrix multiplication (including BLAS and CuBLAS) are
optimized for dense.
Second, even with large memory budget, we sometimes spend almost 20-30% time in
caching.
[~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as blocker
for SystemML 1.0. Please feel free to assign this issue to yourself.
was:
We have identified two performance bugs that frequently occurs in deep learning
script.
First, we repeatedly perform unnecessary conversion to sparse format. Also, the
operations such as matrix multiplication (including BLAS and CuBLAS) are
optimized for dense.
Second, even with large memory budget, we sometimes spend almost 20-30% time in
caching.
[~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as blocker
for SystemML 1.0.
> Sparse/Caching performance bugs related to deep learning scripts
> ----------------------------------------------------------------
>
> Key: SYSTEMML-1140
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1140
> Project: SystemML
> Issue Type: Bug
> Affects Versions: SystemML 1.0
> Reporter: Niketan Pansare
> Priority: Blocker
>
> We have identified two performance bugs that frequently occurs in deep
> learning script.
> First, we repeatedly perform unnecessary conversion to sparse format. Also,
> the operations such as matrix multiplication (including BLAS and CuBLAS) are
> optimized for dense.
>
> Second, even with large memory budget, we sometimes spend almost 20-30% time
> in caching.
> [~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as
> blocker for SystemML 1.0. Please feel free to assign this issue to yourself.
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