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Re: st: MP running no faster than IC

From   Lucas <>
Subject   Re: st: MP running no faster than IC
Date   Mon, 8 Jul 2013 21:37:38 -0700

Stata isn't over-claiming.  They just probably never though that
someone running a command that takes 2 seconds would be seeking to run
it even faster.  My jobs, and jobs of other people I know, routinely
run days or weeks.  (And, yes, it is identified, everything checks
out, it is just the data is massive and the model appropriately
complex).  It is for such jobs that one needs parallel processing.
Running the same 2 second command 500 times can't be parallelized with
any efficiency because the overhead of managing the allocation of
tasks swamps any gains attributable to parallelization.  Stata's only
fault--if fault it be--is not making clear that unless one uses big
data or finds oneself in situations that one model takes days or
weeks, MP is of dubious value.  But, on the other hand, users seeking
to run a regression model in .1 second rather than 2 seconds only
inspire one to ask, "Why?"

On Mon, Jul 8, 2013 at 9:09 PM, Ted Player <> wrote:
> The benchmark tests I originally described were conducted on a local
> machine.  I did a follow-up with an EC2 machine (as described
> elsewhere in this thread).
> I see now that buried on p. 231 of Stata's MP performance report is
> the mention that to get the improvements that Stata claims for
> regression requires a single regression model with 180 regressors and
> a dataset with 1,500,000 observations.  I usually do things like
> bootstrap analyses on datasets with 500 observations, so I guess MP
> isn't any more useful to me than SE.
> It looks like I fell for the advertising hype on
> .  It's my fault for thinking Stata
> wouldn't overclaim to make their software seem better than it really
> is.  Live and learn I guess!
> On Mon, Jul 8, 2013 at 9:42 PM, Sergiy Radyakin <> wrote:
>> Dear Ted, I've witnessed many times that MP works much faster the IC.
>> The figures in the report do make sense. No looking at your example:
>> the only parallelizable part here is the "regress mpg weight gear
>> foreign." Two things to notice immediately are the following:
>> 1) the dataset contains 74 observations. The overhead of parallelizing
>> it into 12 CPUs or even 4 CPUs is large relative to the size of the
>> task at hand. You are likely to see the benefits of parallelization
>> when you -expand- your dataset, say 1000000 (10^6) times and perhaps
>> reduce the number of bootstrap iterations.
>> 2) the dataset contains 74 observations. So the _regress command
>> (internal) takes, say, 0.00001second and with parallelization takes
>> may be 0.000001 second, but then you have 2 seconds of writing the
>> output to the screen and scrolling the output window.  That is not
>> parallelized (correct me if I am wrong), though scrolling seems to
>> work much faster in recent versions (THANKS!) So, try disabling the
>> output with -quietly- and you will see more performance gain from MP.
>> 3) finally, Stata's ado files seem to not be parallelizable (you don't
>> write them that way), but only internal commands are. There have been
>> some changes in the most recent versions and the idea is to permit the
>> users to write parallel code. I am yet to see these facilities, but it
>> makes no sense to test parallelization benefits on do/ado code or
>> where such code executes for a significant amount of time. This is
>> also a reason while there is no need to separately benchmark bootstrap
>> commands.
>> To summarize the above, try the following commands on LARGE datasets
>> (occupy e.g. half of your memory with data):
>> mlogit - you should see performance increase about 3 times on a 12
>> CPUs MP vs 1CPU IC.
>> summarize - you should see about 11-fold performance increase on a
>> 12CPUs MP vs 1CPU IC
>> Run tests on a local machine. Perhaps it's the Amazon that is to blame
>> (I don't mean it). Some hosters limit your TOTAL computing power, so
>> you can get 128 cores with the same total performance as 1 core. Then
>> you are better of with a single CPU license of course :)
>> Hope this helps.
>> Best, Sergiy Radyakin
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