[Rd] OOP performance, was: V2.9.0 changes [SEC=Unclassified]

Troy Robertson Troy.Robertson at aad.gov.au
Fri Jul 3 02:56:20 CEST 2009

Hi Gabor,

Look, I agree with you about S3 and have at times wished I had chosen that path rather than S4.  It seems to do the things I struggle to find answers for with S4.  But..., knowing little about R before engaging with this project, I decided to go with the latest OO framework, S4.  I do now find that I am undoing some of it, such as the use of data member slots, in order to implement pass-by-ref via environments and improve performance.  But its all a learning experience.


> -----Original Message-----
> From: Gabor Grothendieck [mailto:ggrothendieck at gmail.com]
> Sent: Friday, 3 July 2009 10:29 AM
> To: Troy Robertson; r-devel at R-project.org
> Subject: Re: [Rd] OOP performance, was: V2.9.0 changes [SEC=Unclassified]
> In terms of performance if you want the fastest
> performance in R go with S3 and if you want
> even faster performance rewrite your inner loops
> in C.  All the other approaches will usually be slower.
> Also S3 is simple, elegant and will result in less code
> and take you much less time to design, program and
> debug.
> For 100% R code, particularly for simulations,
> proto can sometimes be even faster than pure R code based
> S3 as proto supports hand optimizations that cannot readily
> be done in other systems.  (For unoptimized code it would
> be slower.)  The key trick is based on its ability
> to separate dispatching from calling so that if method f and
> object p are unchanged in a loop
>    for(...) p$f(...)
> then the loop can be rewritten
>   f <- p$f; for(...) f(...)
> Note that this still retains dynamic dispatch but
> just factors it out of the loop.  With S3 the best you could
> get would be for(...) f.p(...) where f is a method of class p
> but this is really tantamount to not using OO at all since
> no dispatch is done at all.
> On Thu, Jul 2, 2009 at 11:31 AM, Thomas
> Petzoldt<Thomas.Petzoldt at tu-dresden.de> wrote:
> > Hi Troy,
> >
> > first of all a question, what kind of ecosystem models are you
> > developing in R? Differential equations or individual-based?
> >
> > Your write that you are a frustrated Java developer in R. I have a
> > similar experience, however I still like JAVA, and I'm now more happy
> > with R as it is much more efficient (i.e. sum(programming + runtime))
> > for the things I usually do: ecological data analysis and modelling.
> >
> > After using functional R quite a time and Java in parallel
> > I had the same idea, to make R more JAVA like and to model ecosystems in
> > an object oriented manner. At that time I took a look into R.oo (thanks
> > Henrik Bengtssson) and was one of the Co-authors of proto. I still think
> > that R.oo is very good and that proto is a cool idea, but finally I
> > switched to the recommended S4 for my ecological simulation package.
> >
> > Note also, that my solution was *not* to model the ecosystems as objects
> > (habitat - populations- individuals), but instead to model ecological
> > models (equations, inputs, parameters, time steps, outputs, ...).
> >
> > This works quite well with S4. A speed test (see useR!2006 poster on
> > http://simecol.r-forge.r-project.org/) showed that all OOP flavours had
> > quite comparable performance.
> >
> > The only thing I have to have in mind are a few rules:
> >
> > - avoid unnecessary copying of large objects. Sometimes it helps to
> > prefer matrices over data frames.
> >
> > - use vectorization. This means for an individual-based model that one
> > has to re-think how to model an individual: not "many [S4] objects"
> > like in JAVA, but R structures (arrays, lists, data frames) where
> > vectorized functions (e.g. arithmetics or subset) can work with.
> >
> > - avoid interpolation (i.e. approx) and if unavoidable, minimize the
> tables.
> >
> > If all these things do not help, I write core functions in C (others use
> > Fortran). This can be done in a mixed style and even a full C to C
> > communication is possible (see the deSolve documentation how to do this
> > with differential equation models).
> >
> >
> > Thomas P.
> >
> >
> >
> > --
> > Thomas Petzoldt
> > Technische Universitaet Dresden
> > Institut fuer Hydrobiologie        thomas.petzoldt at tu-dresden.de
> > 01062 Dresden                      http://tu-dresden.de/hydrobiologie/
> >
> > ______________________________________________
> > R-devel at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-devel
> >

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