Hi Boris,
On Sat, Mar 22, 2014 at 6:45 AM, Boris Capitanu <bor...@gmail.com> wrote: > Hello, > > Apologies in advance for the long post... to frame my problem and > questions I have to give a lot of contextual info. > > I am running a set of experiments to understand the feasibility and > performance characteristics of running Akka on the Blue > Waters<https://bluewaters.ncsa.illinois.edu/>(batch) supercomputer at > NCSA <http://www.ncsa.illinois.edu/>. > > The general theme for the experiments is to create a map-reduce like > system comprised of a producer, one (or more) coordinator(s), and a number > of workers. (the reduce step could be optional) > The producer is responsible for generating the "work" which is then sent > to the coordinator(s) which in turn assign the work to an idle worker. > At first I am most concerned with the performance of the solution (rather > than fault-tolerance). The only fault-tolerance aspect I'd like to address > at first consists of exceptions thrown as part of executing the "work". > When that happens, the "executor" should be restarted and the coordinator > informed of the failed job which would get logged and perhaps retried later > (up to n times) depending on the exception. Eventually the final solution > would include a checkpointing mechanism that would allow the PBS job to be > resumed from the last checkpoint in case the it is killed or a malfunction > occurs with BlueWaters. > > Besides learning about the different options/architectures that are > appropriate for an environment such as Blue Waters, the problem I > ultimately want to solve is to process approx. 3.2 million gzipped text > files (about 3.2TB of gzipped data). The "processing" could be any number > of things - feature extraction, OCR error correction, or other > text-processing routines as needed. > > About my experience: I am no Scala and Akka expert but I do have a decent > amount of experience with both. I've taken Martin's functional programming > in scala course on Coursera, as well as the reactive programming course by > Martin, Erik, and Roland. I've also read through a number of scala and akka > books, tutorials, examples...etc. but, as with just about everything else > in life, there's always more to learn. > > My first attempt was to adapt the Distributed Workers with Akka and > Scala<http://www.typesafe.com/activator/template/akka-distributed-workers> > example > just to see how that performs "out of the box". In the process I've also > had to find a solution to be able to get the application deployed to the > Blue Waters Torque/PBS batch environment, which wasn't trivial... but I did > come up with 2 ways to do that that seem to work quite well (if anyone > wants to know how, I could write about that). I did manage to get the > example to work - tested it on a small subset of the data (with some > changes to WorkProducer) - but I felt that this solution was too overly > concerned with fault tolerance at the expense of (some) performance, so I > wanted to create another solution where those concerns would be reversed. > (it's still on my TODO list to get some performance measurements of the > original example so that I can compare numbers across the different > solutions) > > The way I'm planning on evaluating the performance of each solution is by > having the work producer generate a known quantity of (empty) work as fast > as possible, have that work be coordinated by the master over a known > number of worker nodes, and have the executor on each worker do nothing > else but respond immediately with an (empty) result. > Be aware of that the network roundtrips might dominate when you test with empty work in a way that might not be relevant when the workers do real work. > From this setup I want to record the timing of the following operations: > > - overall wall clock time from start of work being produced to when > the last result was received by the worker actor from the executor (the > master and workers would already be up, registered, and ready before the > producer starts sending out work) (this time is not affected by other jobs > running on the bluewaters system since the work involves no I/O to disk - > just network - and the compute nodes where the worker actors are deployed > are exclusively dedicated to this task - are not running other user jobs) > - overall stats about the time elapsed between when a Work object is > sent to the master by the producer, and the moment the Master receives it > - overall stats about the time elapsed between Master sending work to > Worker, and Worker responding with WorkIsDone > > In (pseudo-scala) code, the solution I have created behaves like this (the > relevant parts): > > *Producer* > > val numWorkToGenerate = N > > for (i <- 1 to numWorkToGenerate) { > > timestamp = getCurrentTimestamp > > work = new Work(i, timestamp) > > master ! work > > } > > master ! NoMoreWork > > > *Master* > > ... keep track of workers and what they're working on, and keep a work > queue of work that still needs to be assigned ... > > > > def receive = { > > case work: Work => > > record time delta based on current timestamp and timestamp packaged > in 'work' > > find idle worker and, if found, update timstamp in 'work' with > current timestamp and send 'work' to worker > > > case NoMoreWork => if the work queue is empty and none of the workers > are working on anything then output all recorded times to log and quit > gracefully > > > case WorkIsDone => > > record time delta based on current timestamp and timestamp packaged > in 'work' > > if work queue is not empty and there is an idle worker then do the > right thing as in the 'case work' above, otherwise check if all work is > done and run the NoMoreWork logic > > } > > > *Worker* > > ... create an Executor actor that will be used to execute the work and > keep track of what it's working on ... > > > > def idle = { > > case work: Work => > > executor ! work > > context become working > > } > > > def working = { > > case ExecuteDone => > > master ! WorkDone > > context become idle > > } > > > *Executor* > > def receive = { > > case work: Work => sender ! ExecuteDone > > } > > > For those interested the actual code is here: > https://github.com/borice/akka-grid-workers > I noticed one error when use onSuccess callback. See here http://doc.akka.io/docs/akka/2.3.0/general/jmm.html#Actors_and_shared_mutable_state > > When I run this, each physical compute node will run one instance of the > Worker app per CPU core; > The Master and Producer will be run on the same compute node. (other > Worker instances will also run on that node, on the 'remaining' CPU cores) > > Compared to the original example I started from: > > - my first solution tries to have as little communication as possible > - there is no acknowledgment from Master to Producer for each 'work' > received; producer operates in a fire-and-forget style > - the pull-model used in the original example is still there, but has > been simplified to be triggered by (not shown above) RegisterWorker, > WorkDone, and WorkFailed messages rather than using the WorkerRequestsWork > / WorkIsReady mechanism > - the result of the work is not given to the Master; the master is > only informed that the work was done (this is because the simplest version > of work may not need to produce a result to someone --- the result could > just be an output written to a file or DB or whatever; i will explore the > use of a pub-sub system to publish the result in a future version of the > solution) > - FYI: i did some renaming in the code: Master has been baptized as > "Coordinator" and WorkProducer became "Producer" > > I'm not concerned with how realistic the above assumptions are right now > -- I just want to see what the performance is of the leanest solution i can > think of, learn about its shortcomings, and use that to keep iterating to > improve on it. I'm also curious about the reliability of the communication > between the compute nodes, and how the akka cluster behaves with no > code-added fault tolerance as I scale up the number of nodes. I suspect > that on Blue Waters the reliability guarantees are higher than if I were > creating an akka cluster over a bunch of commodity computers. > > So... finally now I can describe what the problem I'm having is (thanks > for reading this far!) > > Basically I ran a test of the code on GitHub on BlueWaters on 3 physical > nodes, each node having 32 cores (and 64GB RAM). > I'm using the "<role>.min-nr-of-members" config setting to ensure that all > nodes that join the akka cluster are transitioned to "Up" only after all > workers, coordinator, and producer join the cluster. > I've used the "registerOnMemberUp" callback as the trigger to create the > Producer, Coordinator (aka Master), and Worker actors. When Workers and > Producer actors start, they look up the coordinator ActorRef and the > Producer uses that as the trigger to start producing the work. > I've used the "producer.workCount" config setting to try different > quantities of work and for small values (<100,000), everything works fine. > When I tried using workCount=1,000,000 the Producer would start getting > disassociated from the cluster -- it would basically detect everyone else > as UNREACHABLE, or I would see many entries like the following in the logs: > > [ReliableDeliverySupervisor:71] [WARN] - Association with remote system > [akka.tcp://cluster@nid25948:38358] has failed, address is now gated for > [5000] ms. Reason is: [Disassociated]. > > > It would seem that either the Producer becomes congested trying to send > out the messages to the coordinator, or maybe the coordinator somehow > becomes congested in a way that affects the Producer. > yes, you might need to add some flow control, or throttling. > It's definitely the case that this solution isn't too viable since a > single coordinator will have trouble keeping up with so many messages, > especially given that it also needs to be able to exchange messages with > the workers (and "in production" there might be thousands of workers, or > more). > In theory this should've still worked... as far as I understand it, the > actor mailbox size is only limited by the amount of memory available (and > I've been generous with that - and saw no OOM messages anywhere or process > crashes). > long garbage collection pauses can also trigger failure detection > > I wonder whether the akka cluster extension in this case could be playing > a part in the issues I'm seeing. The heartbeats or gossip messages > exchanged between the cluster nodes might add unneeded traffic to a > potentially-already-saturated network. I think that the simple solution I > presented above could be implemented without using the cluster extension, > by just using remote actors. The Producer only needs to know about the > Coordinator (and monitor its lifecycle), and similar for the Workers. I > wonder what the practical limits are for the maximum size of an akka > cluster, assuming there are no JVM-related limits, when the only limit > becomes the communication protocol/medium... > http://typesafe.com/blog/running-a-2400-akka-nodes-cluster-on-google-compute-engine > > I'll be collecting some stats for values of "workCount" that do not cause > errors for future comparison... > > It seems to me that the next thing I should try is to lessen the stress on > the coordinator. I'm thinking a better solution would be to plan to have > one Coordinator instance on each physical compute node, and have the > workers that are started on that physical node register with it... then on > the physical node where the Producer is started, also start a router where > the routees are created on each of the physical nodes as Coordinators. > Then the Producer would send the work to the router which could use > RoundRobin (or other policies) to route the work to coordinators. > > Finally, as part of the mix of solutions I'll be experimenting with I'd > like to also simulate the same basic setup in an MPI-based application, and > do some timing comparison just for the heck of it. Its very likely that > the MPI solution will perform better (especially when using the highly > optimized Cray MPI library over the Gemini router ASIC) but it would be > educational to see the magnitude of the difference. I would be interested > to explore the possibility of adding MPI-based communication to Akka as an > alternative to TCP, if the performance difference is significant. > > I'll be posting updates on this thread as I have more results. > Thanks for an interesting post. Regards, Patrik > I'm looking for comments and suggestions based on what I wrote above... > > Thank you in advance! Again, sorry for the long post. > > -Boris > > > > > > > > > > > > > > > > > > > > > > -- > >>>>>>>>>> Read the docs: http://akka.io/docs/ > >>>>>>>>>> Check the FAQ: > http://doc.akka.io/docs/akka/current/additional/faq.html > >>>>>>>>>> Search the archives: https://groups.google.com/group/akka-user > --- > You received this message because you are subscribed to the Google Groups > "Akka User List" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to akka-user+unsubscr...@googlegroups.com. > To post to this group, send email to akka-user@googlegroups.com. > Visit this group at http://groups.google.com/group/akka-user. > For more options, visit https://groups.google.com/d/optout. > -- Patrik Nordwall Typesafe <http://typesafe.com/> - Reactive apps on the JVM Twitter: @patriknw -- >>>>>>>>>> Read the docs: http://akka.io/docs/ >>>>>>>>>> Check the FAQ: >>>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html >>>>>>>>>> Search the archives: https://groups.google.com/group/akka-user --- You received this message because you are subscribed to the Google Groups "Akka User List" group. 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