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https://issues.apache.org/jira/browse/SPARK-26228?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16706701#comment-16706701
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Chen Lin commented on SPARK-26228:
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[~shahid]
I have upload the screenshot of log.
I doubt there are extra costs when writing a size of 16000*16000*8 byte array.
> OOM issue encountered when computing Gramian matrix
> ----------------------------------------------------
>
> Key: SPARK-26228
> URL: https://issues.apache.org/jira/browse/SPARK-26228
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.3.0
> Reporter: Chen Lin
> Priority: Major
> Attachments: 1.jpeg
>
>
> {quote}/**
> * Computes the Gramian matrix `A^T A`.
> *
> * @note This cannot be computed on matrices with more than 65535 columns.
> */
> {quote}
> As the above annotation of computeGramianMatrix in RowMatrix.scala said, it
> supports computing on matrices with no more than 65535 columns.
> However, we find that it will throw OOM(Request Array Size Exceeds VM Limit)
> when computing on matrices with 16000 columns.
> The root casue seems that the TreeAggregate writes a very long buffer array
> (16000*16000*8) which exceeds jvm limit(2^31 - 1).
> Does RowMatrix really supports computing on matrices with no more than 65535
> columns?
> I doubt that computeGramianMatrix has a very serious performance issue.
> Do anyone has done some performance expriments before?
>
>
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