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Posted to issues@systemml.apache.org by "Mike Dusenberry (JIRA)" <ji...@apache.org> on 2016/09/29 20:41:21 UTC
[jira] [Updated] (SYSTEMML-994) GC OOM: Binary Matrix to Frame
Conversion
[ https://issues.apache.org/jira/browse/SYSTEMML-994?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Mike Dusenberry updated SYSTEMML-994:
-------------------------------------
Description:
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
cc [~mboehm7], [~acs_s]
was:
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$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
Script:
{code}
train = read("train")
val = read("val")
trainf = as.frame(train)
valf = as.frame(val)
// Rest of algorithm, which passes the frames to DML functions, and performs row indexing to pull out batches, convert to matrices, and train.
{code}
cc [~mboehm7], [~acs_s]
> GC OOM: Binary Matrix to Frame Conversion
> -----------------------------------------
>
> Key: SYSTEMML-994
> URL: https://issues.apache.org/jira/browse/SYSTEMML-994
> Project: SystemML
> Issue Type: Bug
> Reporter: Mike Dusenberry
> Priority: Blocker
>
> I currently have a SystemML matrix saved to HDFS in binary block format, and am attempting to read it in, convert it to a {{frame}}, and then pass that to an algorithm so that I can pull batches out of it with minimal overhead.
> When attempting to run this, I am repeatedly hitting the following GC limit:
> {code}
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at org.apache.sysml.runtime.matrix.data.FrameBlock.ensureAllocatedColumns(FrameBlock.java:281)
> at org.apache.sysml.runtime.matrix.data.FrameBlock.copy(FrameBlock.java:979)
> at org.apache.sysml.runtime.matrix.data.FrameBlock.copy(FrameBlock.java:965)
> at org.apache.sysml.runtime.matrix.data.FrameBlock.<init>(FrameBlock.java:91)
> at org.apache.sysml.runtime.instructions.spark.utils.FrameRDDAggregateUtils$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
> cc [~mboehm7], [~acs_s]
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