[SYSTEMDS-994] GC OOM: Binary Matrix to Frame Conversion

• Type: Bug
• Status: Closed
• Priority: Blocker
• Resolution: Fixed
• Fix Version/s: SystemML 0.11
• Epic Link: Frame data type support
• Assignee: Matthias Boehm
• Reporter: Mike Dusenberry
• Created: 29/Sep/16 20:35
• Updated: 20/Dec/16 20:23
• Resolved: 03/Oct/16 03:32

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:

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)

Script:

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.

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

Issue Links

• relates to: SYSTEMDS-914 MLContext Performance Improvements
• relates to: SYSTEMDS-925 Performance frame converters (various formats)