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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/02/08 11:14:34 UTC
[jira] [Resolved] (SPARK-5656) NegativeArraySizeException in
EigenValueDecomposition.symmetricEigs for large n and/or large k
[ https://issues.apache.org/jira/browse/SPARK-5656?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-5656.
------------------------------
Resolution: Fixed
Fix Version/s: 1.4.0
Issue resolved by pull request 4433
[https://github.com/apache/spark/pull/4433]
> NegativeArraySizeException in EigenValueDecomposition.symmetricEigs for large n and/or large k
> ----------------------------------------------------------------------------------------------
>
> Key: SPARK-5656
> URL: https://issues.apache.org/jira/browse/SPARK-5656
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: Mark Bittmann
> Priority: Minor
> Fix For: 1.4.0
>
>
> Large values of n or k in EigenValueDecomposition.symmetricEigs will fail with a NegativeArraySizeException. Specifically, this occurs when 2*n*k > Integer.MAX_VALUE. These values are currently unchecked and allow for the array to be initialized to a value greater than Integer.MAX_VALUE. I have written the below 'require' to fail this condition gracefully. I will submit a pull request.
> require(ncv * n.toLong < Integer.MAX_VALUE, "Product of 2*k*n must be smaller than " +
> s"Integer.MAX_VALUE. Found required eigenvalues k = $k and matrix dimension n = $n")
> Here is the exception that occurs from computeSVD with large k and/or n:
> Exception in thread "main" java.lang.NegativeArraySizeException
> at org.apache.spark.mllib.linalg.EigenValueDecomposition$.symmetricEigs(EigenValueDecomposition.scala:85)
> at org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:258)
> at org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:190)
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