Matt, I’m interested in following up on this. If you can’t do a PR, can you describe what you did a bit more?
On Aug 21, 2017, at 12:05 PM, Pat Ferrel <p...@occamsmachete.com> wrote: Matt I’ll create a feature branch of Mahout in my git repo for simplicity (we are in code freeze for Mahout right now) Then if you could peel off you changes and make a PR against it. Everyone can have a look before any change is made to the ASF repos. Do a PR against this https://github.com/pferrel/mahout/tree/sparse-speedup <https://github.com/pferrel/mahout/tree/sparse-speedup>, even if it’s not working we can take a look. The branch right now is just a snapshot of the current master in code freeze. Mahout has always had methods to work with different levels of sparsity and you may have found a missing point to optimize. Let’s hope so. On Aug 21, 2017, at 11:47 AM, Andrew Palumbo <ap....@outlook.com> wrote: I should mention that the densisty is currently set quite high, and we've been discussing a user defined setting for this. Something that we have not worked in yet. ________________________________ From: Andrew Palumbo <ap....@outlook.com> Sent: Monday, August 21, 2017 2:44:35 PM To: user@mahout.apache.org Subject: Re: spark-itemsimilarity scalability / Spark parallelism issues (SimilarityAnalysis.cooccurrencesIDSs) We do currently have optimizations based on density analysis in use e.g.: in AtB. https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b45863df3e53/math-scala/src/main/scala/org/apache/mahout/math/scalabindings/package.scala#L431 +1 to PR. thanks for pointing this out. --andy ________________________________ From: Pat Ferrel <p...@occamsmachete.com> Sent: Monday, August 21, 2017 2:26:58 PM To: user@mahout.apache.org Subject: Re: spark-itemsimilarity scalability / Spark parallelism issues (SimilarityAnalysis.cooccurrencesIDSs) That looks like ancient code from the old mapreduce days. If is passes unit tests create a PR. Just a guess here but there are times when this might not speed up thing but slow them down. However for vey sparse matrixes that you might see in CF this could work quite well. Some of the GPU optimization will eventually be keyed off the density of a matrix, or selectable from knowing it’s characteristics. I use this code all the time and would be very interested in a version that works with CF style very sparse matrices. Long story short, create a PR so the optimizer guys can think through the implications. If I can also test it I have some large real-world data where I can test real-world speedup. On Aug 21, 2017, at 10:53 AM, Pat Ferrel <p...@occamsmachete.com> wrote: Interesting indeed. What is “massive”? Does the change pass all unit tests? On Aug 17, 2017, at 1:04 PM, Scruggs, Matt <matt.scru...@bronto.com> wrote: Thanks for the remarks guys! I profiled the code running locally on my machine and discovered this loop is where these setQuick() and getQuick() calls originate (during matrix Kryo deserialization), and as you can see the complexity of this 2D loop can be very high: https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b45863df3e53/math/src/main/java/org/apache/mahout/math/AbstractMatrix.java#L240 Recall that this algorithm uses SparseRowMatrix whose rows are SequentialAccessSparseVector, so all this looping seems unnecessary. I created a new subclass of SparseRowMatrix that overrides that assign(matrix, function) method, and instead of looping through all the columns of each row, it calls SequentialAccessSparseVector.iterateNonZero() so it only has to touch the cells with values. I also had to customize MahoutKryoRegistrator a bit with a new default serializer for this new matrix class. This yielded a massive performance boost and I verified that the results match exactly for several test cases and datasets. I realize this could have side-effects in some cases, but I'm not using any other part of Mahout, only SimilaritAnalysis.cooccurrencesIDSs(). Any thoughts / comments? Matt On 8/16/17, 8:29 PM, "Ted Dunning" <ted.dunn...@gmail.com> wrote: > It is common with large numerical codes that things run faster in memory on > just a few cores if the communication required outweighs the parallel > speedup. > > The issue is that memory bandwidth is slower than the arithmetic speed by a > very good amount. If you just have to move stuff into the CPU and munch on > it a bit it is one thing, but if you have to move the data to CPU and back > to memory to distributed it around possibly multiple times, you may wind up > with something much slower than you would have had if you were to attack > the problem directly. > > > > On Wed, Aug 16, 2017 at 4:47 PM, Pat Ferrel <p...@occamsmachete.com> wrote: > >> This uses the Mahout blas optimizing solver, which I just use and do not >> know well. Mahout virtualizes some things having to do with partitioning >> and I’ve never quite understood how they work. There is a .par() on one of >> the matrix classes that has a similar function to partition but in all >> cases I’ve used .par(auto) and use normal spark repartitioning based on >> parallelism. Mahout implements a mapBlock function, which (all things being >> equal) looks at a partition at a time in memory. The reduce is not part of >> the code I wrote. >> >> The reduce is not part of the code I wrote. Maybe someone else can explain >> what blas is doing. >> >> BTW hashmap is O(log n) on average for large n—caveats apply. We use >> fastutils for many things (I thought this was one case) because they are >> faster than JVM implementations but feel free to dig in further. We use >> downsampling to maintain an overall rough O(n) calculation speed where n = >> # rows (users). As the model gets more dense there are greatly diminishing >> returns for the density so after the elements per row threshold is reached >> we don’t use more in the model creation math. >> >> Still feel free to dig into what the optimizer is doing. >> >> >> On Aug 15, 2017, at 11:13 AM, Scruggs, Matt <matt.scru...@bronto.com> >> wrote: >> >> Thanks Pat, that's good to know! >> >> This is the "reduce" step (which gets its own stage in my Spark >> jobs...this stage takes almost all the runtime) where most of the work is >> being done, and takes longer the more shuffle partitions there are >> (relative to # of CPUs): >> >> https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b4 >> 5863df3e53/spark/src/main/scala/org/apache/mahout/ >> sparkbindings/blas/AtA.scala#L258 >> >> >> Why does the runtime of this reduce stage (that ultimately calls >> SequentialAccessSparseVector.getQuick() and setQuick() a lot) depend on >> the ratio of (# Spark CPUs / spark.sql.shuffle.partitions)? Essentially >> that ratio determines how many "chunks" of shuffle partition (reduce) tasks >> must run, and each of those chunks always takes the same amount of time, so >> the stage finishes in less time when that ratio is low (preferably 1). >> >> EXAMPLES - using 32 cores and 200 shuffle partitions, this stage requires >> ceil(145 tasks / 32 cores) = 5 "chunks" of work (145 tasks instead of 200 >> because of the estimateProductPartitions call in AtA). Each chunk takes ~8 >> minutes, so (5 chunks * 8 min) = ~40 mins. For a job with 32 cores and 32 >> shuffle partitions (same CPU resources, still 100% utilized), this stage >> requires only ceil(23 tasks / 32 cores) = 1 chunk of work, which takes the >> same 8 minutes, so the job finishes ~5x faster. You can take this to the >> extreme with just 1 core and 1 shuffle partition, and the stage still takes >> the same amount of time! I'd love to know if you can reproduce this >> behavior. >> >> This goes against most advice and experience I've had with Spark, where >> you want to *increase* your partitioning in many cases (or at least leave >> it at the default 200, not lower it dramatically) to utilize CPUs better >> (and shrink each individual partition's task). There seems to be no >> reduction in computational complexity *per task* (within this stage I'm >> talking about) even with high values for spark.sql.shuffle.partitions (so >> it seems the data isn't actually being partitioned by the shuffle process). >> Refer back to the timings w/various configs in my first message. >> >> Also...is there a possibility of using a faster hash-based implementation >> instead of the setQuick() / getQuick() methods of >> SequentialAccessSparseVector? The javadoc on those methods mentions they >> shouldn't be used unless absolutely necessary due to their O(log n) >> complexity. >> >> >> Thanks for your time...this is fun stuff! >> Matt >> >> >> >> On 8/15/17, 10:15 AM, "Pat Ferrel" <p...@occamsmachete.com> wrote: >> >>> Great, this is the best way to use the APIs. The big win with CCO, the >> algo you are using is with multiple user actions. Be aware that when you go >> to this methods the input IndexedDatasets must be coerced to have >> compatible dimensionality, in this case the primary action defines the >> user-set used in calculating the model—not the one for making queries, >> which can use anonymous user history. But that is for later and outside >> Mahout. >>> >>> 1) 4x max parallelism is a rule of thumb since the cores may not need >> 100% duty cycle, if they are already at 100% the 4x does no good. 2) you >> have found a long running task but there will always be one, if it weren’t >> this one it would be another. Different types of tasks use resources >> differently. For instance the collects, which must eventually use a the >> memory of the Driver to instantiate an in-memory data structure. There is >> no magic choice to make this work differently but it avoid several joins, >> which are much slower. >>> >>> I’m not quite sure what your question is. >>> >>> >>> On Aug 15, 2017, at 6:21 AM, Scruggs, Matt <matt.scru...@bronto.com> >> wrote: >>> >>> Hi Pat, >>> >>> I've taken some screenshots of my Spark UI to hopefully shed some light >> on the behavior I'm seeing. Do you mind if I send you a link via direct >> email (would rather not post it here)? It's just a shared Dropbox folder. >>> >>> >>> Thanks, >>> Matt >>> >>> >>> >>> On 8/14/17, 11:34 PM, "Scruggs, Matt" <matt.scru...@bronto.com> wrote: >>> >>>> I'm running a custom Scala app (distributed in a shaded jar) directly >> calling SimilarityAnalysis.cooccurrenceIDSs(), not using the CLI. >>>> >>>> The input data already gets explicitly repartitioned to spark.cores.max >> (defaultParallelism) in our code. I'll try increasing that by the factor of >> 4 that you suggest, but all our cores are already utilized so I'm not sure >> that will help. It gets bogged down in the post-shuffle (shuffle read / >> combine / reduce) phase even with all cores busy the whole time, which is >> why I've been playing around with various values for >> spark.sql.shuffle.partitions. The O(log n) operations I mentioned seem to >> take >95% of runtime. >>>> >>>> Thanks, >>>> Matt >>>> ________________________________ >>>> From: Pat Ferrel <p...@occamsmachete.com> >>>> Sent: Monday, August 14, 2017 11:02:42 PM >>>> To: user@mahout.apache.org >>>> Subject: Re: spark-itemsimilarity scalability / Spark parallelism >> issues (SimilarityAnalysis.cooccurrencesIDSs) >>>> >>>> Are you using the CLI? If so it’s likely that there is only one >> partition of the data. If you use Mahout in the Spark shell or using it as >> a lib, do a repartition on the input data before passing it into >> SimilarityAnalysis.cooccurrencesIDSs. I repartition to 4*total cores to >> start with and set max parallelism for spark to the same. The CLI isn’t >> really production worthy, just for super easy experiments with CSVs. >>>> >>>> >>>> On Aug 14, 2017, at 2:31 PM, Scruggs, Matt <matt.scru...@bronto.com> >> wrote: >>>> >>>> Howdy, >>>> >>>> I'm running SimilarityAnalysis.cooccurrencesIDSs on a fairly small >> dataset (about 870k [user, item] rows in the primary action IDS…no cross >> co-occurrence IDS) and I noticed it scales strangely. This is with Mahout >> 0.13.0 although the same behavior happens in 0.12.x as well (haven't tested >> it before that). >>>> >>>> TLDR - regardless of the Spark parallelism (CPUs) I throw at this >> routine, every Spark task within the final / busy stage seems to take the >> same amount of time, which leads me to guess that every shuffle partition >> contains the same amount of data (perhaps the full dataset matrix in >> shape/size, albeit with different values). I'm reaching out to see if this >> is a known algorithmic complexity issue in this routine, or if my config is >> to blame (or both). >>>> >>>> Regarding our hardware, we have identical physical machines in a Mesos >> cluster with 6 workers and a few masters. Each worker has ~500GB of SSD, 32 >> cores and 128g RAM. We run lots of Spark jobs and have generally ironed out >> the kinks in terms of hardware and cluster config, so I don't suspect any >> hardware-related issues. >>>> >>>> Here are some timings for SimilarityAnalysis.cooccurrencesIDSs on this >> dataset with maxNumInteractions = 500, maxInterestingItemsPerThing = 20, >> randomSeed = default, parOpts = default (there's lots of other Spark >> config, this is just what I'm varying to check for effects). In particular, >> notice how the ratio of (spark.sql.shuffle.partitions / spark.cores.max) >> affects the runtime: >>>> >>>> * 8 executors w/8 cores each, takes about 45 minutes >>>> * note that spark.sql.shuffle.partitions > spark.cores.max >>>> spark.cores.max = 64 >>>> spark.executor.cores = 8 >>>> spark.sql.shuffle.partitions = 200 (default) >>>> >>>> * 1 executors w/24 cores, takes about 65 minutes >>>> * note that spark.sql.shuffle.partitions >>> spark.cores.max >>>> spark.cores.max = 24 >>>> spark.executor.cores = 24 >>>> spark.sql.shuffle.partitions = 200 (default) >>>> >>>> * 1 executor w/8 cores, takes about 8 minutes >>>> * note that spark.sql.shuffle.partitions = spark.cores.max >>>> spark.cores.max = 8 >>>> spark.executor.cores = 8 (1 executor w/8 cores) >>>> spark.sql.shuffle.partitions = 8 >>>> >>>> * 1 executor w/24 cores, takes about 8 minutes (same as 8 cores!) >>>> * note that spark.sql.shuffle.partitions = spark.cores.max >>>> spark.cores.max = 24 >>>> spark.executor.cores = 24 (1 executor w/24 cores) >>>> spark.sql.shuffle.partitions = 24 >>>> >>>> * 32 executors w/2 cores each, takes about 8 minutes (same as 8 cores!) >>>> * note that spark.sql.shuffle.partitions = spark.cores.max >>>> spark.cores.max = 64 >>>> spark.executor.cores = 2 >>>> spark.sql.shuffle.partitions = 88 (results in 64 tasks for final stage) >>>> >>>> Adjusting the "maxNumInteractions" parameter down to 100 and 50 results >> in a minor improvement (5-10%). I've also played around with removing >> [user, item] rows from the input dataset for users with only 1 >> interaction…I read to try that in another thread…that yielded maybe a >> 40-50% speed improvement, but I'd rather not toss out data (unless it truly >> is totally useless, of course :D ). >>>> >>>> When I look at the thread dump within the Spark UI's Executors -> >> thread dump pages, it seems all the executors are very busy in the code >> pasted below for >95% of the run. GC throughput is very good so we're not >> bogged down there...it's just super busy doing running the code below. I am >> intrigued about the comments on the SequentialAccessSparseVector methods I >> see being called (getQuick and setQuick), which state they take O(log n) >> time (https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b4 >> 5863df3e53/math/src/main/java/org/apache/mahout/math/ >> SequentialAccessSparseVector.java). >>>> >>>> >>>> Thanks all for your time and feedback! >>>> >>>> Matt Scruggs >>>> >>>> org.apache.mahout.math.OrderedIntDoubleMapping.find( >> OrderedIntDoubleMapping.java:105) >>>> org.apache.mahout.math.OrderedIntDoubleMapping.get( >> OrderedIntDoubleMapping.java:110) >>>> org.apache.mahout.math.SequentialAccessSparseVector.getQuick( >> SequentialAccessSparseVector.java:157) >>>> org.apache.mahout.math.SparseRowMatrix.getQuick( >> SparseRowMatrix.java:90) >>>> org.apache.mahout.math.AbstractMatrix.assign(AbstractMatrix.java:240) >>>> org.apache.mahout.math.scalabindings.MatrixOps.$plus$ >> eq(MatrixOps.scala:45) >>>> org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala: >> 258) >>>> org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala: >> 258) >>>> org.apache.spark.util.collection.ExternalAppendOnlyMap$$ >> anonfun$3.apply(ExternalAppendOnlyMap.scala:151) >>>> org.apache.spark.util.collection.ExternalAppendOnlyMap$$ >> anonfun$3.apply(ExternalAppendOnlyMap.scala:150) >>>> org.apache.spark.util.collection.AppendOnlyMap. >> changeValue(AppendOnlyMap.scala:144) >>>> org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue( >> SizeTrackingAppendOnlyMap.scala:32) >>>> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll( >> ExternalAppendOnlyMap.scala:163) >>>> org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:50) >>>> org.apache.spark.shuffle.BlockStoreShuffleReader.read( >> BlockStoreShuffleReader.scala:85) >>>> org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:109) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.scheduler.ShuffleMapTask.runTask( >> ShuffleMapTask.scala:79) >>>> org.apache.spark.scheduler.ShuffleMapTask.runTask( >> ShuffleMapTask.scala:47) >>>> org.apache.spark.scheduler.Task.run(Task.scala:86) >>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) >>>> java.util.concurrent.ThreadPoolExecutor.runWorker( >> ThreadPoolExecutor.java:1142) >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run( >> ThreadPoolExecutor.java:617) >>>> java.lang.Thread.run(Thread.java:745) >>>> >>>> ……or this code…… >>>> >>>> org.apache.mahout.math.SparseRowMatrix.setQuick( >> SparseRowMatrix.java:105) >>>> org.apache.mahout.math.AbstractMatrix.assign(AbstractMatrix.java:240) >>>> org.apache.mahout.math.scalabindings.MatrixOps.$plus$ >> eq(MatrixOps.scala:45) >>>> org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala: >> 258) >>>> org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala: >> 258) >>>> org.apache.spark.util.collection.ExternalAppendOnlyMap$$ >> anonfun$3.apply(ExternalAppendOnlyMap.scala:151) >>>> org.apache.spark.util.collection.ExternalAppendOnlyMap$$ >> anonfun$3.apply(ExternalAppendOnlyMap.scala:150) >>>> org.apache.spark.util.collection.AppendOnlyMap. >> changeValue(AppendOnlyMap.scala:144) >>>> org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue( >> SizeTrackingAppendOnlyMap.scala:32) >>>> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll( >> ExternalAppendOnlyMap.scala:163) >>>> org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:50) >>>> org.apache.spark.shuffle.BlockStoreShuffleReader.read( >> BlockStoreShuffleReader.scala:85) >>>> org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:109) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.rdd.MapPartitionsRDD.compute( >> MapPartitionsRDD.scala:38) >>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:283) >>>> org.apache.spark.scheduler.ShuffleMapTask.runTask( >> ShuffleMapTask.scala:79) >>>> org.apache.spark.scheduler.ShuffleMapTask.runTask( >> ShuffleMapTask.scala:47) >>>> org.apache.spark.scheduler.Task.run(Task.scala:86) >>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) >>>> java.util.concurrent.ThreadPoolExecutor.runWorker( >> ThreadPoolExecutor.java:1142) >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run( >> ThreadPoolExecutor.java:617) >>>> java.lang.Thread.run(Thread.java:745) >>>> >>> >> >>
