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https://issues.apache.org/jira/browse/SYSTEMML-946?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15511091#comment-15511091
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Matthias Boehm commented on SYSTEMML-946:
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what is the script you're execution here?

> OOM on spark dataframe-matrix / csv-matrix conversion
> -----------------------------------------------------
>
>                 Key: SYSTEMML-946
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-946
>             Project: SystemML
>          Issue Type: Bug
>          Components: Runtime
>            Reporter: Matthias Boehm
>
> The decision on dense/sparse block allocation in our dataframeToBinaryBlock 
> and csvToBinaryBlock data converters is purely based on the sparsity. This 
> works very well for the common case of tall & skinny matrices. However, for 
> scenarios with dense data but huge number of columns a single partition will 
> rarely have 1000 rows to fill an entire row of blocks. This leads to 
> unnecessary allocation and dense-sparse conversion as well as potential 
> out-of-memory errors because the temporary memory requirement can be up to 
> 1000x larger than the input partition.



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