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From "Janardhan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-1140) Sparse/Caching performance bugs related to deep learning scripts
Date Sat, 24 Feb 2018 04:56:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Janardhan updated SYSTEMML-1140:
--------------------------------
    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] [~mwdusenb@us.ibm.com] I am labeling this bug as blocker for SystemML
1.0. Please feel free to assign this issue to yourself.

*Improvements so far:*

1. Disabled sparse conversions & caching,  by [commit|https://github.com/apache/systemml/commit/caaaec90b61e529e50021d89f9f108230fa307a8]

2. binary sparse-dense mult/div, preallocation by [commit |https://github.com/apache/systemml/commit/4f86485939d4777d2799a697b2cbc23ea93ee7e4]

3. For `conv_2d_bias_add`, the `elementWiseInPlaceTransposedAddition` first 

  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] [~mwdusenb@us.ibm.com] I am labeling this bug as blocker for SystemML
1.0. Please feel free to assign this issue to yourself.


> 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 1.0.0, SystemML 1.1
>            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] [~mwdusenb@us.ibm.com] I am labeling this bug as blocker for SystemML
1.0. Please feel free to assign this issue to yourself.
> *Improvements so far:*
> 1. Disabled sparse conversions & caching,  by [commit|https://github.com/apache/systemml/commit/caaaec90b61e529e50021d89f9f108230fa307a8]
> 2. binary sparse-dense mult/div, preallocation by [commit |https://github.com/apache/systemml/commit/4f86485939d4777d2799a697b2cbc23ea93ee7e4]
> 3. For `conv_2d_bias_add`, the `elementWiseInPlaceTransposedAddition` first 



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