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Posted to issues@systemml.apache.org by "Matthias Boehm (JIRA)" <ji...@apache.org> on 2016/02/17 08:07:18 UTC
[jira] [Comment Edited] (SYSTEMML-512) DML Script With UDFs Results
In Out Of Memory Error As Compared to Without UDFs
[ https://issues.apache.org/jira/browse/SYSTEMML-512?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15150016#comment-15150016 ]
Matthias Boehm edited comment on SYSTEMML-512 at 2/17/16 7:06 AM:
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[~mwdusenb@us.ibm.com] could you please specify the configuration of size and rank that you used? I just tried it with size=10k and memory budget of 2g and everything worked fine. However, I could image an issue if the dense matrix fits in memory because we would not mark the function for recompilation and our IPA does not yet propagate literals into functions, but already tracked in [SYSTEMML-427].
With regard to the out-of-memory - this is most likely an issue of the open jdk young generation size as explained in [SYSTEMML-455]
was (Author: mboehm7):
[~mwdusenb@us.ibm.com] could you please specify the configuration of size and rank that you used? I just tried it with size=10k and memory budget of 2g and everything worked fine. However, I could image an issue if the dense matrix fits in memory because we would not mark the function for recompilation and our IPA does not yet propagate literals into functions, but already tracked in [SYSTEMML-427].
> DML Script With UDFs Results In Out Of Memory Error As Compared to Without UDFs
> -------------------------------------------------------------------------------
>
> Key: SYSTEMML-512
> URL: https://issues.apache.org/jira/browse/SYSTEMML-512
> Project: SystemML
> Issue Type: Bug
> Reporter: Mike Dusenberry
>
> Currently, the following script for running a simple version of Poisson non-negative matrix factorization (PNMF) runs in linear time as desired:
> {code}
> # data & args
> X = read($X)
> X = X+1 # change product IDs to be 1-based, rather than 0-based
> V = table(X[,1], X[,2])
> V = V[1:$size,1:$size]
> max_iteration = as.integer($maxiter)
> rank = as.integer($rank)
> # run PNMF
> n = nrow(V)
> m = ncol(V)
> range = 0.01
> W = Rand(rows=n, cols=rank, min=0, max=range, pdf="uniform")
> H = Rand(rows=rank, cols=m, min=0, max=range, pdf="uniform")
> i=0
> while(i < max_iteration) {
> H = (H * (t(W) %*% (V/(W%*%H))))/t(colSums(W))
> W = (W * ((V/(W%*%H)) %*% t(H)))/t(rowSums(H))
> i = i + 1;
> }
> # compute negative log-likelihood
> negloglik_temp = -1 * (sum(V*log(W%*%H)) - as.scalar(colSums(W)%*%rowSums(H)))
> # write outputs
> negloglik = matrix(negloglik_temp, rows=1, cols=1)
> write(negloglik, $negloglikout)
> write(W, $Wout)
> write(H, $Hout)
> {code}
> However, a small refactoring of this same script to pull the core PNMF algorithm and the negative log-likelihood computation out into separate UDFs results in non-linear runtime and a Java out of memory heap error on the same dataset.
> {code}
> pnmf = function(matrix[double] V, integer max_iteration, integer rank) return (matrix[double] W, matrix[double] H) {
> n = nrow(V)
> m = ncol(V)
>
> range = 0.01
> W = Rand(rows=n, cols=rank, min=0, max=range, pdf="uniform")
> H = Rand(rows=rank, cols=m, min=0, max=range, pdf="uniform")
>
> i=0
> while(i < max_iteration) {
> H = (H * (t(W) %*% (V/(W%*%H))))/t(colSums(W))
> W = (W * ((V/(W%*%H)) %*% t(H)))/t(rowSums(H))
> i = i + 1;
> }
> }
> negloglikfunc = function(matrix[double] V, matrix[double] W, matrix[double] H) return (double negloglik) {
> negloglik = -1 * (sum(V*log(W%*%H)) - as.scalar(colSums(W)%*%rowSums(H)))
> }
> # data & args
> X = read($X)
> X = X+1 # change product IDs to be 1-based, rather than 0-based
> V = table(X[,1], X[,2])
> V = V[1:$size,1:$size]
> max_iteration = as.integer($maxiter)
> rank = as.integer($rank)
> # run PNMF and evaluate
> [W, H] = pnmf(V, max_iteration, rank)
> negloglik_temp = negloglikfunc(V, W, H)
> # write outputs
> negloglik = matrix(negloglik_temp, rows=1, cols=1)
> write(negloglik, $negloglikout)
> write(W, $Wout)
> write(H, $Hout)
> {code}
> The expectation would be that such modularization at the DML level should be allowed without any impact on performance.
> Details:
> - Data: Amazon product co-purchasing dataset from Stanford [http://snap.stanford.edu/data/amazon0601.html | http://snap.stanford.edu/data/amazon0601.html]
> - Execution mode: Spark {{MLContext}}, but should be applicable to command-line invocation as well.
> - Error message:
> {code}
> java.lang.OutOfMemoryError: Java heap space
> at org.apache.sysml.runtime.matrix.data.MatrixBlock.allocateDenseBlock(MatrixBlock.java:415)
> at org.apache.sysml.runtime.matrix.data.MatrixBlock.sparseToDense(MatrixBlock.java:1212)
> at org.apache.sysml.runtime.matrix.data.MatrixBlock.examSparsity(MatrixBlock.java:1103)
> at org.apache.sysml.runtime.instructions.cp.MatrixMatrixArithmeticCPInstruction.processInstruction(MatrixMatrixArithmeticCPInstruction.java:60)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:309)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:227)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:169)
> at org.apache.sysml.runtime.controlprogram.WhileProgramBlock.execute(WhileProgramBlock.java:183)
> at org.apache.sysml.runtime.controlprogram.FunctionProgramBlock.execute(FunctionProgramBlock.java:115)
> at org.apache.sysml.runtime.instructions.cp.FunctionCallCPInstruction.processInstruction(FunctionCallCPInstruction.java:177)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:309)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:227)
> at org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:169)
> at org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:146)
> at org.apache.sysml.api.MLContext.executeUsingSimplifiedCompilationChain(MLContext.java:1387)
> at org.apache.sysml.api.MLContext.compileAndExecuteScript(MLContext.java:1252)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1184)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1165)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:113)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:103)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.immutable.Range.foreach(Range.scala:141)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:103)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:135)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:137)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:139)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:141)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:143)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:145)
> {code}
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