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From Dhruv Kumar <dku...@ecs.umass.edu>
Subject Re: LDA on single node is much faster than 20 nodes
Date Tue, 06 Sep 2011 23:12:53 GMT
On Tue, Sep 6, 2011 at 6:57 PM, Chris Lu <clu@atypon.com> wrote:

> Thanks. Very helpful to me!
>
> I tried to change the setting of "mapred.map.tasks".  However, the number
> map task is still just one on one of the 20 machines.
>
> ./elastic-mapreduce --create --alive \
>   --num-instances 20 --name "LDA" \
>   --bootstrap-action s3://elasticmapreduce/**bootstrap-actions/configure-*
> *hadoop \
>   --bootstrap-name "Configuring number of map tasks per job" \
>   --args "-m,mapred.map.tasks=40"
>
> Anyone knows how to configure the number of mappers?
> Again, the input size is only 46M.
>
> Chris
>
>
> On 09/06/2011 12:09 PM, Ted Dunning wrote:
>
>> Well, I think that using small instances is a disaster in general.  The
>> performance that you get from them can vary easily by an order of
>> magnitude.
>>  My own preference for real work is either m2xl or cc14xl.  The latter
>> machines give you nearly bare metal performance and no noisy neighbors.
>>  The
>> m2xl is typically very much underpriced on the spot market.
>>
>> Sean is right about your job being misconfigured.  The Hadoop overhead is
>> considerable and you have only given it two threads to overcome that
>> overhead.
>>
>> On Tue, Sep 6, 2011 at 6:12 PM, Sean Owen<srowen@gmail.com>  wrote:
>>
>>  That's your biggest issue, certainly. Only 2 mappers are running, even
>>> though you have 20 machines available. Hadoop determines the number of
>>> mappers based on input size, and your input isn't so big that it thinks
>>> you
>>> need 20 workers. It's launching 33 reducers, so your cluster is put to
>>> use
>>> there. But it's no wonder you're not seeing anything like 20x speedup in
>>> the
>>> mapper.
>>>
>>> You can of course force it to use more mappers, and that's probably a
>>> good
>>> idea here. -Dmapred.map.tasks=20 perhaps. More mappers means more
>>> overhead
>>> of spinning up mappers to process less data, and Hadoop's guess indicates
>>> that it thinks it's not efficient to use 20 workers. If you know that
>>> those
>>> other 18 are otherwise idle, my guess is you'd benefit from just making
>>> it
>>> use 20.
>>>
>>

Sean,

I too have always been confused about how Hadoop decides to set the number
of mappers so you could help my understanding here...

Is -Dmapred.map.tasks just a hint to the framework for the number of mappers
(just like using the combiner is a hint) or does it actually set the number
of workers to that number (provided our input is large enough)?

The reason I ask is because on
http://wiki.apache.org/hadoop/HowManyMapsAndReduces, it is mentioned that
the framework uses the HDFS block size to decide on the number of mapper
workers to be invoked. Should we be setting that parameter instead?


>
>>> If this were a general large cluster where many people are taking
>>> advantage
>>> of the workers, then I'd trust Hadoop's guesses until you are sure  you
>>> want
>>> to do otherwise.
>>>
>>> On Tue, Sep 6, 2011 at 7:02 PM, Chris Lu<clu@atypon.com>  wrote:
>>>
>>>  Thanks for all the suggestions!
>>>>
>>>> All the inputs are the same. It takes 85 hours for 4 iterations on 20
>>>> Amazon small machines. On my local single node, it got to iteration 19
>>>>
>>> for
>>>
>>>> also 85 hours.
>>>>
>>>> Here is a section of the Amazon log output.
>>>> It covers the start of iteration 1, and between iteration 4 and
>>>> iteration
>>>> 5.
>>>>
>>>> The number of map tasks is set to 2. Should it be larger or related to
>>>> number of CPU cores?
>>>>
>>>>
>>>>
>

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