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https://issues.apache.org/jira/browse/SYSTEMML-994?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Matthias Boehm resolved SYSTEMML-994.
-------------------------------------
Resolution: Fixed
Fix Version/s: SystemML 0.11
> GC OOM: Binary Matrix to Frame Conversion
> -----------------------------------------
>
> Key: SYSTEMML-994
> URL: https://issues.apache.org/jira/browse/SYSTEMML-994
> Project: SystemML
> Issue Type: Bug
> Reporter: Mike Dusenberry
> Assignee: Matthias Boehm
> Priority: Blocker
> Fix For: SystemML 0.11
>
>
> I currently have a SystemML matrix saved to HDFS in binary block format, and
> am attempting to read it in, convert it to a {{frame}}, and then pass that to
> an algorithm so that I can pull batches out of it with minimal overhead.
> When attempting to run this, I am repeatedly hitting the following GC limit:
> {code}
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at
> org.apache.sysml.runtime.matrix.data.FrameBlock.ensureAllocatedColumns(FrameBlock.java:281)
> at
> org.apache.sysml.runtime.matrix.data.FrameBlock.copy(FrameBlock.java:979)
> at
> org.apache.sysml.runtime.matrix.data.FrameBlock.copy(FrameBlock.java:965)
> at
> org.apache.sysml.runtime.matrix.data.FrameBlock.<init>(FrameBlock.java:91)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDAggregateUtils$CreateBlockCombinerFunction.call(FrameRDDAggregateUtils.java:57)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDAggregateUtils$CreateBlockCombinerFunction.call(FrameRDDAggregateUtils.java:48)
> at
> org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction$1.apply(JavaPairRDD.scala:1015)
> at
> org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:187)
> at
> org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:186)
> at
> org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:148)
> at
> org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32)
> at
> org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192)
> at
> org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> Script:
> {code}
> train = read("train")
> val = read("val")
> trainf = as.frame(train)
> valf = as.frame(val)
> // Rest of algorithm, which passes the frames to DML functions, and performs
> row indexing to pull out batches, convert to matrices, and train.
> {code}
> Cluster setup:
> * Spark Standalone
> * 1 Master, 9 Workers
> * 47 cores, 124 GB available to Spark on each Worker (1 core + 1GB saved for
> OS)
> * spark.driver.memory 80g
> * spark.executor.memory 21g
> * spark.executor.cores 3
> * spark.default.parallelism 20000
> * spark.driver.maxResultSize 0
> * spark.akka.frameSize 128
> * spark.network.timeout 1000s
> Note: This is using today's latest build as of 09.29.16 1:30PM PST.
> cc [~mboehm7], [~acs_s]
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