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Posted to issues@systemml.apache.org by "Matthias Boehm (JIRA)" <ji...@apache.org> on 2016/09/20 23:05:20 UTC

[jira] [Created] (SYSTEMML-946) OOM on spark dataframe-matrix / csv-matrix conversion

Matthias Boehm created SYSTEMML-946:
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             Summary: 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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