The mert optimization is unlikely to be a bottleneck unless you've  
changed the size of the n-best list, which I believe defaults to 100.   
At that setting it can usually finish in a few minutes on a single  
processor.  At a high setting of 1000, it can take over an hour in  
later iterations.


Cheers
Adam

On 9 Jun 2008, at 02:30, Philipp Koehn wrote:

> Hi,
>
> there is also a stage in the mert training (the actual mert  
> optimization,
> not running the decoder) that is currently not parallelized, so you
> cannot expect any gains on that. It is my understanding that we will
> have a better implementation of mert that also parallelizes the
> optimization loop, but Nicola could correct me on that.
>
> -phi
>
> On Sun, Jun 8, 2008 at 7:52 AM, Yee Seng Chan  
> <[EMAIL PROTECTED]> wrote:
>> Thanks, increasing to 1G seem to "unstuck" the processes.
>> My training pairs is only about 250K of sentences, so I had thought  
>> 0.5G should
>> be sufficient; perhaps the issue is with the size of the LM.
>>
>> Nevertheless, I'm doing mert on a dev-set which has only about 1000  
>> source
>> sentences. For this setup, I found that parallel mert only  
>> marginally reduces
>> the time for each mert iteration. Non-parallel mert needs 60  
>> minutes per
>> iteration; while parallel-mert on 5 nodes needs 40-50 minutes per  
>> iteration.
>>
>> I know that the speedup from parallel mert is non-linear in the no.  
>> of nodes,
>> due to the time needed to coordinate among the sub-processes.
>>
>> Best regards,
>> Yee Seng.
>>
>>
>>
>>
>>
>>
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>>
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