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From "Chen Lin (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-26228) OOM issue encountered when computing Gramian matrix
Date Mon, 03 Dec 2018 05:55:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-26228?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Chen Lin updated SPARK-26228:
-----------------------------
    Attachment: 1.jpeg

> 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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