[Rcpp-devel] R vectorisation vs. C++ vectorisation

Darren Cook darren at dcook.org
Tue Nov 20 00:38:01 CET 2012

> The example is not particularly well chosen, but I think the problem
> of vectorisation is a real one.  To vectorise code in R you need to
> have a big R vocabulary; to vectorise code in Rcpp, you need to be
> able to write a loop.  So even if it's a not a completely fair
> comparison to R, it's still reasonable because it's much easy to
> vectorise in C++.

In my case, informed by interesting benchmarks such as yours, I now tend
to write my R functions with optimizing to C++ in mind. In other words,
I've stopped being ashamed of having loops in my R code.

If my R script completes Quick Enough I don't care. But when it takes
Too Long, I know I can improve it by one order of magnitude by writing
better R code, or by two orders of magnitude by porting just the
bottleneck to C++, using Rcpp and inline. For me, at least, those two
choices are about equal effort.


P.S. I don't think the sugar versions can be made any quicker, because
they have to allocate intermediate vectors, and do more memory copies.

>> | exploration of vectorisation in R vs C++ at
>> | https://gist.github.com/4111256
>> | ...
>> |                     expr    min     lq median     uq     max neval
>> |  vacc1(age, female, ily) 6816.8 7139.4 7285.7 7823.9 10055.5   100
>> |  vacc2(age, female, ily)  194.5  202.6  212.6  227.9   260.4   100
>> |  vacc3(age, female, ily)   21.8   22.4   23.4   24.9    35.5   100
>> |  vacc4(age, female, ily)   36.2   38.7   41.3   44.5    55.6   100
>> |  vacc5(age, female, ily)   29.3   31.3   34.0   36.4    52.1   100

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