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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/01/26 08:37:34 UTC
[jira] [Commented] (SPARK-5406) LocalLAPACK mode in
RowMatrix.computeSVD should have much smaller upper bound
[ https://issues.apache.org/jira/browse/SPARK-5406?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14291539#comment-14291539 ]
Apache Spark commented on SPARK-5406:
-------------------------------------
User 'hhbyyh' has created a pull request for this issue:
https://github.com/apache/spark/pull/4200
> LocalLAPACK mode in RowMatrix.computeSVD should have much smaller upper bound
> -----------------------------------------------------------------------------
>
> Key: SPARK-5406
> URL: https://issues.apache.org/jira/browse/SPARK-5406
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.2.0
> Environment: centos, others should be similar
> Reporter: yuhao yang
> Priority: Minor
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> In RowMatrix.computeSVD, under LocalLAPACK mode, the code would invoke brzSvd. Yet breeze svd for dense matrix has latent constraint. In it's implementation:
> val workSize = ( 3
> * scala.math.min(m, n)
> * scala.math.min(m, n)
> + scala.math.max(scala.math.max(m, n), 4 * scala.math.min(m, n)
> * scala.math.min(m, n) + 4 * scala.math.min(m, n))
> )
> val work = new Array[Double](workSize)
> as a result, column num must satisfy 7 * n * n + 4 * n < Int.MaxValue
> thus, n < 17515.
> This jira is only the first step. If possbile, I hope spark can handle matrix computation up to 80K * 80K.
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