Hyunsik Choi updated GIRAPH-12:

    Attachment: GIRAPH-12_3.patch


Thank you for your comments. Yes, I agreed that the aggregated wasted memory 
should be considered =)

In the latest comment, I investigated the real occupation of thread stack by 
using Sleep class (https://gist.github.com/1249761). When I created 2000 Sleep 
threads with the default stack size option '-Xss4096k', the memory usage of 
both the process and all threads is only 46 mega bytes.

So, I would like to say that the individual thread consumes much less stack 
size than default thread stack size. The default thread stack size affects the 
virtual memory area size. It is not resident memory size. The actual stack size 
per thread seems to be only affected by local variables and function 

As a result, I guess that the memory problem is usually caused by outgoing 
messages kept in memory =)

Anyway, I attach the patch. The main difference from the previous patch is that 
the default number of thread pool is set to the number of workers - 1 if  
unset. Besides, I added more comments.

The unit tests are passed against the real hadoop cluster.
> Investigate communication improvements
> --------------------------------------
>                 Key: GIRAPH-12
>                 URL: https://issues.apache.org/jira/browse/GIRAPH-12
>             Project: Giraph
>          Issue Type: Improvement
>          Components: bsp
>            Reporter: Avery Ching
>            Assignee: Hyunsik Choi
>            Priority: Minor
>         Attachments: GIRAPH-12_1.patch, GIRAPH-12_2.patch, GIRAPH-12_3.patch
> Currently every worker will start up a thread to communicate with every other 
> workers.  Hadoop RPC is used for communication.  For instance if there are 
> 400 workers, each worker will create 400 threads.  This ends up using a lot 
> of memory, even with the option  
> -Dmapred.child.java.opts="-Xss64k".  
> It would be good to investigate using frameworks like Netty or custom roll 
> our own to improve this situation.  By moving away from Hadoop RPC, we would 
> also make compatibility of different Hadoop versions easier.

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