Hi Hieu
That's exactly why I took to pre-pruning the phrase table, as I
mentioned on Friday. I had something like 750,000 translations of the
most common word, and it took half-an-hour to get the first sentence
translated.
cheers - Barry
On 05/10/15 15:48, Hieu Hoang wrote:
what pt implementation did you use, and had it been pre-pruned so that
there's a limit on how many target phrase for a particular source
phrase? ie. don't have 10,000 entries for 'the' .
I've been digging around multithreading in the last few weeks. I've
noticed that the compact pt is VERY bad at handling unpruned pt.
Cores
1 5 10 15 20 25
Unpruned compact pt 143 42 32 38 52 62
probing pt 245 58 33 25 24 21
Pruned compact pt 119 24 15 10 10 10
probing pt 117 25 25 10 10 10
Hieu Hoang
http://www.hoang.co.uk/hieu
On 5 October 2015 at 15:15, Michael Denkowski
<[email protected] <mailto:[email protected]>>
wrote:
Hi all,
Like some other Moses users, I noticed diminishing returns from
running Moses with several threads. To work around this, I added
a script to run multiple single-threaded instances of moses
instead of one multi-threaded instance. In practice, this sped
things up by about 2.5x for 16 cpus and using memory mapped models
still allowed everything to fit into memory.
If anyone else is interested in using this, you can prefix a moses
command with scripts/generic/multi_moses.py. To use multiple
instances in mert-moses.pl <http://mert-moses.pl>, specify
--multi-moses and control the number of parallel instances with
--decoder-flags='-threads N'.
Below is a benchmark on WMT fr-en data (2M training sentences,
400M words mono, suffix array PT, compact reordering, 5-gram
KenLM) testing default stack decoding vs cube pruning without and
with the parallelization script (+multi):
---
1cpu sent/sec
stack 1.04
cube 2.10
---
16cpu sent/sec
stack 7.63
+multi 12.20
cube 7.63
+multi 18.18
---
--Michael
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