Mike, I'm not sure what you mean about removing foo but I think the method
is sound in diagnosing a program issue and the results speak for themselves.
I did invert my if statement at the suggestion of a CS professor (who also
suggested recoding in C, but I'm in an applied math program and haven't had
the time to take programming courses, which i know would be helpful)
Anyway, with the statement as:
if( !(k %in% c(10^4,10^5,10^6,10^7)) ){
#do nothing
} else {
q <- q+1
Out[[q]] <- M
}
run times were back to around 20 minutes. So as best I can tell something
happens in the if statement causing the computer to work ahead, as the
professor suggests. I'm no expert on R (and have no desire to try looking at
the R source code (it would only confuse me)) but if anyone can offer
guidance on how the if statement works (Does R try to work ahead? Under what
conditions does it try to "work ahead" so I can try to exploit this
behavior) I would greatly appreciate it.
If it would require too much knowledge of the computer system to understand
I doubt I would be able to make use of it, but maybe someone else could
benefit.
On Tue, Oct 26, 2010 at 3:24 PM, Mike Marchywka <marchy...@hotmail.com>wrote:
>
>
>
>
>
>
>
> ----------------------------------------
> > Date: Tue, 26 Oct 2010 12:53:14 -0400
> > From: mike...@gmail.com
> > To: j...@bitwrit.com.au
> > CC: r-help@r-project.org
> > Subject: Re: [R] runtime on ising model
> >
> > I have an update on where the issue is coming from.
> >
> > I commented out the code for "pos[k+1] <- M[i,j]" and the if statement
> for
> > time = 10^4, 10^5, 10^6, 10^7 and the storage and everything ran
> fast(er).
> > Next I added back in the "pos" statements and still runtimes were good
> > (around 20 minutes).
> >
> > So I'm left with something is causing problems in:
>
> I haven't looked at this since some passing interest in magnetics
> decades ago, something about 8-tracks and cassettes, but you have
> to be careful with conclusions like " I removed foo and problem
> went away therefore problem was foo." Performance issues are often
> caused by memory, not CPU limitations. Removing anything with a big
> memory footprint could speed things up. IO can be a real bottleneck.
> If you are talking about things on minute timescales, look at task
> manager and see if you are even CPU limited. Look for page faults
> or IO etc. If you really need performance and have a task which
> is relatively simple, don't ignore c++ as a way to generate data
> points and then import these into R for analysis.
>
> In short, just because you are focusing on math it doesn't mean
> the computer is limited by that.
>
>
> >
> > ## Store state at time 10^4, 10^5, 10^6, 10^7
> > if( k %in% c(10^4,10^5,10^6,10^7) ){
> > q <- q+1
> > Out[[q]] <- M
> > }
> >
> > Would there be any reason R is executing the statements inside the "if"
> > before getting to the logical check?
> > Maybe R is written to hope for the best outcome (TRUE) and will just
> throw
> > out its work if the logic comes up FALSE?
> > I guess I can always break the for loop up into four parts and store the
> > state at the end of each, but thats an unsatisfying solution to me.
> >
> >
> > Jim, I like the suggestion of just pulling one big sample, but since I
> can
> > get the runtimes under 30 minutes just by removing the storage piece I
> doubt
> > I would see any noticeable changes by pulling large sample vectors.
> >
> > Thanks,
> > Michael
> >
> > On Tue, Oct 26, 2010 at 6:22 AM, Jim Lemon wrote:
> >
> > > On 10/26/2010 04:50 PM, Michael D wrote:
> > >
> > >> So I'm in a stochastic simulations class and I having issues with the
> > >> amount
> > >> of time it takes to run the Ising model.
> > >>
> > >> I usually don't like to attach the code I'm running, since it will
> > >> probably
> > >> make me look like a fool, but I figure its the best way I can find any
> > >> bits
> > >> I can speed up run time.
> > >>
> > >> As for the goals of the exercise:
> > >> I need the state of the system at time=1, 10k, 100k, 1mill, and 10mill
> > >> and the percentage of vertices with positive spin at all t
> > >>
> > >> Just to be clear, i'm not expecting anyone to tell me how to program
> this
> > >> model, cause I know what I have works for this exercise, but it takes
> far
> > >> too long to run and I'd like to speed it up by replacing slow
> operations
> > >> wherever possible.
> > >>
> > >> Hi Michael,
> > > One bottleneck is probably the sampling. If it doesn't grab too much
> > > memory, setting up a vector of the samples (maybe a million at a time
> if 10
> > > million is too big - might be able to rewrite your sample vector when
> you
> > > store the state) and using k (and an offset if you don't have one big
> > > vector) to index it will give you some speed.
> > >
> > > Jim
> > >
> > >
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
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