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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2019/01/23 01:28:00 UTC
[jira] [Resolved] (SPARK-26228) OOM issue encountered when
computing Gramian matrix
[ https://issues.apache.org/jira/browse/SPARK-26228?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-26228.
-------------------------------
Resolution: Fixed
Fix Version/s: 2.3.4
2.4.1
3.0.0
Issue resolved by pull request 23600
[https://github.com/apache/spark/pull/23600]
> OOM issue encountered when computing Gramian matrix
> ----------------------------------------------------
>
> Key: SPARK-26228
> URL: https://issues.apache.org/jira/browse/SPARK-26228
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 2.3.0
> Reporter: Chen Lin
> Assignee: Sean Owen
> Priority: Major
> Fix For: 3.0.0, 2.4.1, 2.3.4
>
> 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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