[R] Need very fast application of 'diff' - ideas?
Martin Morgan
mtmorgan at fhcrc.org
Sun Jan 29 18:23:25 CET 2012
On 01/29/2012 08:12 AM, Kevin Ummel wrote:
> Sorry, guys. I'm not active on the listserve, so my last post was held by the moderator until after Dirk's solution was posted.
>
> Excellent stuff.
Is 'diff' really the bottleneck in your calculations? I would have said
diff was in the class of 'fast' R calculations, so would expect many
other steps in a real analysis, including a poorly constructed input, to
be much more expensive.
Since speed is apparently of the essence, it makes sense to create a
shared library and load that, rather than re-compiling it (via inline)
each time.
The calculation is very straight-forward in C. It makes sense to use the
'.Call' interface to avoid copying on the way in and out, and other R
overhead of the '.C' interface. A simple solution, assuming the correct
argument type (numeric; the original post talks about integer values but
the values actually used (floor(x)) are numeric and presumably in a
speed-is-of-the-essence application the data would be created as the the
type of interest), no NAs, non-0 length input, etc., is (like Hans'
solution, using the .C interface), in file cdiff.c:
#include <Rdefines.h>
SEXP cdiff(SEXP x)
{
const int len = Rf_length(x) - 1;
SEXP ans = Rf_allocVector(REALSXP, len);
double *ansp = REAL(ans), *xp = REAL(x);
for (int i = 0; i < len; ++i)
ansp[i] = xp[i + 1] - xp[i];
return ans;
}
I doubt that, with appropriate optimization flags for the compiler, use
of [] vs. pointer arithmetic would make a difference. With compilation as
R CMD SHLIB cdiff.c
One would probably want to compile this with a high optimization, e.g.,
from the 'Writing R Extensions' manual section 5.5
MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB cdiff.c
Use as
dyn.load("cdiff.so")
## ...
x = rnorm(10000000)
ans <- .Call("cdiff", x)
For me I get
> dyn.load("cdiff.so")
> system.time(x <- rnorm(10000000))
user system elapsed
1.577 0.015 1.594
> system.time(ans0 <- diff(x))
user system elapsed
0.842 0.110 0.952
> system.time(ans1 <- .Call("cdiff", x))
user system elapsed
0.024 0.020 0.044
> identical(ans0, ans1)
[1] TRUE
Note that just creating random data already dominates the calculation,
which is why diff seems such an unlikely candidate for a bottleneck. I
would guess that obtaining memory for the answer is the big bottleneck
in cdiff (or Rcpp); one could work around this but then violate R's
memory model. That I can write C code that is 20x faster than 'diff'
comes at a significant price, in terms of error checking and, yes,
development time.
The timing for python in the original post doesn't capture the full cost
of the calculation; it should include the cost of the subset and view
construction (or whatever the efficient pythonic paradigm is)
Martin
>
> thanks,
> kevin
>
> On Jan 29, 2012, at 8:37 AM, R. Michael Weylandt wrote:
>
>> Have you not followed your own thread? Dirk is Mr. Rcpp himself and he
>> gives an implementation that gives you 25x improvement here as well as
>> tips for getting even more out of it:
>>
>> http://tolstoy.newcastle.edu.au/R/e17/help/12/01/2471.html
>>
>> Michael
>>
>> On Sat, Jan 28, 2012 at 12:28 PM, Kevin Ummel<kevinummel at gmail.com> wrote:
>>> Thanks. I've played around with pure R solutions. The fastest re-write of diff (for the 1 lag case) I can seem to find is this:
>>>
>>> diff2 = function(x) {
>>> y = c(x,NA) - c(NA,x)
>>> y[2:length(x)]
>>> }
>>>
>>> #Compiling via 'cmpfun' doesn't seem to help (or hurt):
>>> require(compiler)
>>> diff2 = cmpfun(diff2)
>>>
>>> But that only gets ~10% improvement over default 'diff' on my machine. Still too slow for my particular application.
>>>
>>> I'm inclined towards Michael's suggestion of inline+Rcpp (or some other use of C under the hood).
>>>
>>> Could someone show me how to go about doing that?
>>>
>>> Thanks!
>>> Kevin
>>>
>>> On Jan 28, 2012, at 9:14 AM, Peter Langfelder wrote:
>>>
>>>> ehm... this doesn't take very many ideas.
>>>>
>>>>
>>>> x = runif(n=10e6, min=0, max=1000)
>>>> x = round(x)
>>>>
>>>> system.time( {
>>>> y = x[-1] - x[-length(x)]
>>>> })
>>>>
>>>> I get about 0.5 seconds on my old laptop.
>>>>
>>>> HTH
>>>>
>>>> Peter
>>>>
>>>>
>>>> On Fri, Jan 27, 2012 at 4:15 PM, Kevin Ummel<kevinummel at gmail.com> wrote:
>>>>> Hi everyone,
>>>>>
>>>>> Speed is the key here.
>>>>>
>>>>> I need to find the difference between a vector and its one-period lag (i.e. the difference between each value and the subsequent one in the vector). Let's say the vector contains 10 million random integers between 0 and 1,000. The solution vector will have 9,999,999 values, since their is no lag for the 1st observation.
>>>>>
>>>>> In R we have:
>>>>>
>>>>> #Set up input vector
>>>>> x = runif(n=10e6, min=0, max=1000)
>>>>> x = round(x)
>>>>>
>>>>> #Find one-period difference
>>>>> y = diff(x)
>>>>>
>>>>> Question is: How can I get the 'diff(x)' part as fast as absolutely possible? I queried some colleagues who work with other languages, and they provided equivalent solutions in Python and Clojure that, on their machines, appear to be potentially much faster (I've put the code below in case anyone is interested). However, they mentioned that the overhead in passing the data between languages could kill any improvements. I don't have much experience integrating other languages, so I'm hoping the community has some ideas about how to approach this particular problem...
>>>>>
>>>>> Many thanks,
>>>>> Kevin
>>>>>
>>>>> In iPython:
>>>>>
>>>>> In [3]: import numpy as np
>>>>> In [4]: arr = np.random.randint(0, 1000, (10000000,1)).astype("int16")
>>>>> In [5]: arr1 = arr[1:].view()
>>>>> In [6]: timeit arr2 = arr1 - arr[:-1]
>>>>> 10 loops, best of 3: 20.1 ms per loop
>>>>>
>>>>> In Clojure:
>>>>>
>>>>> (defn subtract-lag
>>>>> [n]
>>>>> (let [v (take n (repeatedly rand))]
>>>>> (time (dorun (map - v (cons 0 v))))))
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>>>
>>>>> ______________________________________________
>>>>> R-help at r-project.org mailing list
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>>>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
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>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at 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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