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From Alexandros Daglis <alexandros.dag...@epfl.ch>
Subject Re: What a "worker" really is and other interesting runtime information
Date Wed, 28 Nov 2012 13:29:19 GMT
Dear Avery,

I followed your advice, but the application seems to be totally
thread-count-insensitive: I literally observe zero scaling of performance,
while I increase the thread count. Maybe you can point out if I am doing
something wrong.

- Using only 4 cores on a single node at the moment
- Input graph: 14 million vertices, file size is 470 MB
- Running SSSP as follows: hadoop jar
target/giraph-0.1-jar-with-dependencies.jar
org.apache.giraph.examples.SimpleShortestPathsVertex
-Dgiraph.SplitMasterWorker=false -Dgiraph.numComputeThreads=X input output
12 1
where X=1,2,3,12,30
- I notice a total insensitivity to the number of thread I specify.
Aggregate core utilization is always approximately the same (usually around
25-30% => only one of the cores running) and overall execution time is
always the same (~8 mins)

Why is Giraph's performance not scaling? Is the input size / number of
workers inappropriate? It's not an IO issue either, because even during
really low core utilization, time is wasted on idle, not on IO.

Cheers,
Alexandros



On 28 November 2012 11:13, Alexandros Daglis <alexandros.daglis@epfl.ch>wrote:

> Thank you Avery, that helped a lot!
>
> Regards,
> Alexandros
>
>
> On 27 November 2012 20:57, Avery Ching <aching@apache.org> wrote:
>
>> Hi Alexandros,
>>
>> The extra task is for the master process (a coordination task). In your
>> case, since you are using a single machine, you can use a single task.
>>
>> -Dgiraph.SplitMasterWorker=**false
>>
>> and you can try multithreading instead of multiple workers.
>>
>> -Dgiraph.numComputeThreads=12
>>
>> The reason why cpu usage increases is due to netty threads to handle
>> network requests.  By using multithreading instead, you should bypass this.
>>
>> Avery
>>
>>
>> On 11/27/12 9:40 AM, Alexandros Daglis wrote:
>>
>>> Hello everybody,
>>>
>>> I went through most of the documentation I could find for Giraph and
>>> also most of the messages in this email list, but still I have not figured
>>> out precisely what a "worker" really is. I would really appreciate it if
>>> you could help me understand how the framework works.
>>>
>>> At first I thought that a worker has a one-to-one correspondence to a
>>> map task. Apparently this is not exactly the case, since I have noticed
>>> that if I ask for x workers, the job finishes after having used x+1 map
>>> tasks. What is this extra task for?
>>>
>>> I have been trying out the example SSSP application on a single node
>>> with 12 cores. Giving an input graph of ~400MB and using 1 worker, around
>>> 10 GBs of memory are used during execution. What intrigues me is that if I
>>> use 2 workers for the same input (and without limiting memory per map
>>> task), double the memory will be used. Furthermore, there will be no
>>> improvement in performance. I rather notice a slowdown. Are these
>>> observations normal?
>>>
>>> Might it be the case that 1 and 2 workers are very few and I should go
>>> to the 30-100 range that is the proposed number of mappers for a
>>> conventional MapReduce job?
>>>
>>> Finally, a last observation. Even though I use only 1 worker, I see that
>>> there are significant periods during execution where up to 90% of the 12
>>> cores computing power is consumed, that is, almost 10 cores are used in
>>> parallel. Does each worker spawn multiple threads and dynamically balances
>>> the load to utilize the available hardware?
>>>
>>> Thanks a lot in advance!
>>>
>>> Best,
>>> Alexandros
>>>
>>>
>>>
>>
>

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