Le 19/11/12 17:47, Douglas Bates a écrit :
On Mon, Nov 19, 2012 at 9:56 AM, Dirk Eddelbuettel <e...@debian.org
<mailto:e...@debian.org>> wrote:
On 19 November 2012 at 09:31, Hadley Wickham wrote:
| Hi all,
|
| Inspired by "Rcpp is smoking fast for agent-based models in data
| frames" (http://www.babelgraph.org/wp/?p=358), I've been doing some
[ I liked that post, but we got flak afterwards as his example was
not well
chosen. The illustration of the language speed difference does of course
hold. ]
| exploration of vectorisation in R vs C++ at
| https://gist.github.com/4111256
|
| I have five versions of the basic vaccinate function:
|
| * vacc1: vectorisation in R with a for loop
| * vacc2: used vectorised R primitives
| * vacc3: vectorised with loop in C++
| * vacc4: vectorised with Rcpp sugar
| * vacc5: vectorised with Rcpp sugar, explicitly labelled as containing
| no missing values
|
| And the timings I get are as follows:
|
| Unit: microseconds
| 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
|
| Unsurprisingly the R loop (vacc1) is very slow, and proper
| vectorisation speeds it up immensely. Interestingly, however, the C++
| loop still does considerably better (about 10x faster) - I'm not sure
| exactly why this is the case, but I suspect it may be because it
| avoids the many intermediate vectors that R requires. The sugar
| version is about half as fast, but this gets quite a bit faster with
| explicit no missing flags.
|
| I'd love any feedback on my code (https://gist.github.com
<https://gist.github.com/4111256>
/4111256 <https://gist.github.com/4111256>) -
| please let me know if I've missed anything obvious.
I don't have a problem with sugar being a little slower that
hand-rolling.
The code is so much simpler and shorter. And we're still way faster than
vectorised R. I like that place.
Somewhat off-topic/on-topic: I am still puzzled by how the Julia
guys now
revert back from vectorised code to hand-written loops because llvm does
better on those. Speed is good, but concise code with speed is
better in my
book.
Sigh. Speaking as one of the "Julia guys" I should point out two things
(not that they will change Dirk's "cold, dead hands" attitude towards
Julia :-)
That might however raise the interest of some other Rcpp author ^^
1. Comprehensions provide what I feel is a clean syntax for sugar-like
operations in Julia
2. A problem with vectorization is the issue of multiple loops, hence
the number of attempts at implementing delayed evaluation in compiled
code (Eigen) and in add-on's to R.
A translation of Hadley's vacc3 into Julia could be
function vacc3a(age::Float64, female::Bool, ily::Float64){
p = 0.25 + 0.3 * 1 / (1 - exp(0.04 * age)) + 0.1 * ily
p *= female ? 1.25 : 0.75
min(max(0., p), 1.)
}
out = [vacc3a(age[i], female[i], ily[i]) for i in 1:length(age)]
The comprehension collapses the
1. Determine the length of the output vector
2. Allocate the result
3. Loop over indices populating the result
4. Return the result
to a single syntactic element that, in my opinion, is quite readable.
Yes. I'm looking into mapply so that we could do e.g.
NumericVector p = mapply( age, female, ily, fun )
where fun is a function object with the correct signature, dealing with
individual elements. Similar idea.
Hence I would prefer to invoke the 80/20 rule as I think we have better
targets to chase than to narrow that gap. But that's just my $0.02...
If you can't sleep til both version have 20-some microsend medians
then by
all means go crazy ;-)
Dirk
--
Romain Francois
Professional R Enthusiast
+33(0) 6 28 91 30 30
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blog: http://romainfrancois.blog.free.fr
|- http://bit.ly/RE6sYH : OOP with Rcpp modules
`- http://bit.ly/Thw7IK : Rcpp modules more flexible
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