[
https://issues.apache.org/jira/browse/MAHOUT-986?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Shannon Quinn updated MAHOUT-986:
---------------------------------
Description:
Dan Brickley and I have been testing SpectralKMeans with a dbpedia dataset (
http://danbri.org/2012/spectral/dbpedia/ ); effectively, a graph with 4,192,499
nodes. Not surprisingly, the LanczosSolver throws an OutOfMemoryError when it
attempts to instantiate a DenseMatrix of dimensions 4192499-by-4192499 (~17.5
trillion double-precision floating point values). Here's the full stack trace:
{quote}
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at org.apache.mahout.math.DenseMatrix.<init>(DenseMatrix.java:50)
at
org.apache.mahout.math.decomposer.lanczos.LanczosState.<init>(LanczosState.java:45)
at
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:146)
at
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:86)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:65)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:79)
at
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.main(SpectralKMeansDriver.java:53)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:616)
at
org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68)
at org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139)
at org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:188)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:616)
at org.apache.hadoop.util.RunJar.main(RunJar.java:156)
{quote}
Obviously SKM needs a more sustainable and memory-efficient way of performing
an eigen-decomposition of the graph laplacian. For those who are more
knowledgeable in the linear systems solvers of Mahout than I, can the Lanczos
parameters be tweaked to negate the requirement of a full DenseMatrix? Or
should SKM move to SSVD instead?
was:
Dan Brickley and I have been testing SpectralKMeans with a dbpedia dataset (
http://danbri.org/2012/spectral/dbpedia/ ); effectively, a graph with 4,192,499
nodes. Not surprisingly, the LanczosSolver throws an OutOfMemoryError when it
attempts to instantiate a DenseMatrix of dimensions 4192499-by-4192499 (~17.5
trillion double-precision floating point values). Here's the full stack trace:
{{Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at org.apache.mahout.math.DenseMatrix.<init>(DenseMatrix.java:50)
at
org.apache.mahout.math.decomposer.lanczos.LanczosState.<init>(LanczosState.java:45)
at
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:146)
at
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:86)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:65)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:79)
at
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.main(SpectralKMeansDriver.java:53)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:616)
at
org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68)
at org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139)
at org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:188)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:616)
at org.apache.hadoop.util.RunJar.main(RunJar.java:156)}}
Obviously SKM needs a more sustainable and memory-efficient way of performing
an eigen-decomposition of the graph laplacian. For those who are more
knowledgeable in the linear systems solvers of Mahout than I, can the Lanczos
parameters be tweaked to negate the requirement of a full DenseMatrix? Or
should SKM move to SSVD instead?
> OutOfMemoryError in LanczosState by way of SpectralKMeans
> ---------------------------------------------------------
>
> Key: MAHOUT-986
> URL: https://issues.apache.org/jira/browse/MAHOUT-986
> Project: Mahout
> Issue Type: Improvement
> Components: Clustering
> Affects Versions: 0.6
> Environment: Ubuntu 11.10 (64-bit)
> Reporter: Shannon Quinn
> Assignee: Shannon Quinn
> Priority: Minor
> Fix For: 0.7
>
>
> Dan Brickley and I have been testing SpectralKMeans with a dbpedia dataset (
> http://danbri.org/2012/spectral/dbpedia/ ); effectively, a graph with
> 4,192,499 nodes. Not surprisingly, the LanczosSolver throws an
> OutOfMemoryError when it attempts to instantiate a DenseMatrix of dimensions
> 4192499-by-4192499 (~17.5 trillion double-precision floating point values).
> Here's the full stack trace:
> {quote}
> Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
> at org.apache.mahout.math.DenseMatrix.<init>(DenseMatrix.java:50)
> at
> org.apache.mahout.math.decomposer.lanczos.LanczosState.<init>(LanczosState.java:45)
> at
> org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:146)
> at
> org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.run(SpectralKMeansDriver.java:86)
> at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:65)
> at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:79)
> at
> org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver.main(SpectralKMeansDriver.java:53)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:616)
> at
> org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68)
> at org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139)
> at org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:188)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:616)
> at org.apache.hadoop.util.RunJar.main(RunJar.java:156)
> {quote}
> Obviously SKM needs a more sustainable and memory-efficient way of performing
> an eigen-decomposition of the graph laplacian. For those who are more
> knowledgeable in the linear systems solvers of Mahout than I, can the Lanczos
> parameters be tweaked to negate the requirement of a full DenseMatrix? Or
> should SKM move to SSVD instead?
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