Vadim Ogranovich
2005-Apr-22 21:02 UTC
[Rd] RE: [R] when can we expect Prof Tierney's compiled R?
If we are on the subject of byte compilation, let me bring a couple of examples which have been puzzling me for some time. I'd like to know a) if the compilation will likely to improve the performance for this type of computations, and b) at least roughly understand the reasons for the observed numbers, specifically why x[i]<- assignment is so much slower than x[i] extraction. The loops below are typical in any recursive calculation where the new value of a vector is based on its immediate neighbor say to the left. Specifically we assign the previous value to the current element. # this shows that the assignment x[i]<- is the bottleneck in the loop> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) x[i]= x[i-1]) [1] 4.29 0.00 4.30 0.00 0.00> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA)x[i-1]) [1] 1.46 0.01 1.46 0.00 0.00 # the overhead of the loop itself is reasonably low, just 0.17 sec> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) 1)[1] 0.17 0.01 0.17 0.00 0.00 # pure assignment (w/o the extraction x[i]) takes 3.09 sec. Thus x[i] as extraction is (3.09 - 0.17)/(0.79 - 0.17) = 4.7 times faster than x[i]<- as assignment. This looks a bit odd.> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) x[i]= 1.0) [1] 3.08 0.00 3.09 0.00 0.00 # this shows that just evaluation of (i-1) takes about (0.79 - 0.24) 0.55 sec on my machine (AMD 64 bit). Looks too slow.> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) i-1)[1] 0.79 0.00 0.79 0.00 0.00> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) i)[1] 0.24 0.01 0.24 0.00 0.00 Thanks, Vadim> -----Original Message----- > From: r-help-bounces@stat.math.ethz.ch > [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Luke Tierney > Sent: Friday, April 22, 2005 7:33 AM > To: Peter Dalgaard > Cc: Jason Liao; r-help@stat.math.ethz.ch > Subject: Re: [R] when can we expect Prof Tierney's compiled R? > > On Wed, 20 Apr 2005, Peter Dalgaard wrote: > > > Luke Tierney <luke@stat.uiowa.edu> writes: > > > >> Vectorized operations in R are also as fast as compiled C (because > >> that is what they are :-)). A compiler such as the one > I'm working > >> on will be able to make most difference for > non-vectorizable or not > >> very vectorizable code. It may also be able to reduce the > need for > >> intermediate allocations in vectorizable code, which may > have other > >> benefits beyond just speed improvements. > > > > Actually, it has struck me a couple of times that these > operations are > > not as fast as they could be, since they are outside the > scope of fast > > BLAS routines, but "embarrassingly parallel" code could easily be > > written for the relevant hardware. Even on uniprocessor > systems there > > might be speedups that the C compiler cannot find (e.g. because it > > cannot assume that source and destination of the operation are > > distinct). > > My guess is that for anything beyond basic operations we are > doing OK on uniprocessors. but it would be useful to do some > testing to be sure. For the basic operations I suspect we > are paying a heavy price for the way we handle recycling, > both in terms of overhead as such and in terms of inhibiting > compiler optimizations. For performance it would probably be > better to code the scalar-vector, equal-length-vector, and > general cases separately, though keeping the code > maintainable may be a bit of a challenge. Again testing on a > range of platforms and compilers would be useful. > > With multiprocessors likely to become more widely available > it would be good to look into ways of factoring the > vectorized math code so we can slide in one that uses threads > when approprate. This should dovetail nicely with > compilation to identify larger vectorized expressions that > can be parallelized as a unit; I hope to look into this a bit > this summer. > > luke > > > > -- > Luke Tierney > Chair, Statistics and Actuarial Science > Ralph E. Wareham Professor of Mathematical Sciences > University of Iowa Phone: 319-335-3386 > Department of Statistics and Fax: 319-335-3017 > Actuarial Science > 241 Schaeffer Hall email: luke@stat.uiowa.edu > Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html >
Luke Tierney
2005-Apr-27 02:50 UTC
[Rd] RE: [R] when can we expect Prof Tierney's compiled R?
For what it's worth (probably not much as these simple benchmarks are
rarely representative of real code and so need to be taken with a huge
chunk of salt) here is what happens with your examples in R 2.1.0 with
the current byte compiler.
Define your examples as functions:
n = 1e6; iA = seq(2,n); x = double(n);
f1 <- function(x, iA) for (i in iA) x[i] = x[i-1]
f2 <- function(x, iA) for (i in iA) x[i-1]
f3 <- function(x, iA) for (i in iA) 1
f4 <- function(x, iA) for (i in iA) x[i] = 1.0
f5 <- function(x, iA) for (i in iA) i-1
f6 <- function(x, iA) for (i in iA) i
Make byte compiled versions:
f1c <- cmpfun(f1)
f2c <- cmpfun(f2)
f3c <- cmpfun(f3)
f4c <- cmpfun(f4)
f5c <- cmpfun(f5)
f6c <- cmpfun(f6)
and run them:
> system.time(f1(x, iA))
[1] 5.43 0.04 5.56 0.00 0.00
> system.time(f1c(x, iA))
[1] 1.77 0.03 1.81 0.00 0.00
> system.time(f2(x, iA))
[1] 1.72 0.01 1.74 0.00 0.00
> system.time(f2c(x, iA))
[1] 0.63 0.00 0.63 0.00 0.00
> system.time(f3(x, iA))
[1] 0.19 0.00 0.20 0.00 0.00
> system.time(f3c(x, iA))
[1] 0.14 0.00 0.15 0.00 0.00
> system.time(f4(x, iA))
[1] 3.78 0.03 3.82 0.00 0.00
> system.time(f4c(x, iA))
[1] 1.26 0.02 1.30 0.00 0.00
> system.time(f5(x, iA))
[1] 0.99 0.00 1.00 0.00 0.00
> system.time(f5c(x, iA))
[1] 0.30 0.00 0.31 0.00 0.00
> system.time(f6(x, iA))
[1] 0.21 0.00 0.23 0.00 0.00
> system.time(f6c(x, iA))
[1] 0.17 0.00 0.20 0.00 0.00
I'll let you do the arithmetic. The byte compiler does get rid of a
fair bit of interpreter overhead (which is large in these kinds of
examples compared to most real code) but there is still considerable
room for improvement. The byte code engine currently uses the same
internal C code for doing the actual work as the interpreter, so
improvements there would help both interpreted and byte compiled code.
Best,
luke
On Fri, 22 Apr 2005, Vadim Ogranovich wrote:
> If we are on the subject of byte compilation, let me bring a couple of
> examples which have been puzzling me for some time. I'd like to know a)
> if the compilation will likely to improve the performance for this type
> of computations, and b) at least roughly understand the reasons for the
> observed numbers, specifically why x[i]<- assignment is so much slower
> than x[i] extraction.
>
> The loops below are typical in any recursive calculation where the new
> value of a vector is based on its immediate neighbor say to the left.
> Specifically we assign the previous value to the current element.
>
> # this shows that the assignment x[i]<- is the bottleneck in the loop
>> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) x[i]
> = x[i-1])
> [1] 4.29 0.00 4.30 0.00 0.00
>> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA)
> x[i-1])
> [1] 1.46 0.01 1.46 0.00 0.00
>
> # the overhead of the loop itself is reasonably low, just 0.17 sec
>> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) 1)
> [1] 0.17 0.01 0.17 0.00 0.00
>
> # pure assignment (w/o the extraction x[i]) takes 3.09 sec. Thus x[i] as
> extraction is (3.09 - 0.17)/(0.79 - 0.17) = 4.7 times faster than x[i]<-
> as assignment. This looks a bit odd.
>> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) x[i]
> = 1.0)
> [1] 3.08 0.00 3.09 0.00 0.00
>
>
> # this shows that just evaluation of (i-1) takes about (0.79 - 0.24) >
0.55 sec on my machine (AMD 64 bit). Looks too slow.
>> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) i-1)
> [1] 0.79 0.00 0.79 0.00 0.00
>> n = 1e6; iA = seq(2,n); x = double(n); system.time(for (i in iA) i)
> [1] 0.24 0.01 0.24 0.00 0.00
>
> Thanks,
> Vadim
>
>> -----Original Message-----
>> From: r-help-bounces@stat.math.ethz.ch
>> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Luke Tierney
>> Sent: Friday, April 22, 2005 7:33 AM
>> To: Peter Dalgaard
>> Cc: Jason Liao; r-help@stat.math.ethz.ch
>> Subject: Re: [R] when can we expect Prof Tierney's compiled R?
>>
>> On Wed, 20 Apr 2005, Peter Dalgaard wrote:
>>
>>> Luke Tierney <luke@stat.uiowa.edu> writes:
>>>
>>>> Vectorized operations in R are also as fast as compiled C
(because
>>>> that is what they are :-)). A compiler such as the one
>> I'm working
>>>> on will be able to make most difference for
>> non-vectorizable or not
>>>> very vectorizable code. It may also be able to reduce the
>> need for
>>>> intermediate allocations in vectorizable code, which may
>> have other
>>>> benefits beyond just speed improvements.
>>>
>>> Actually, it has struck me a couple of times that these
>> operations are
>>> not as fast as they could be, since they are outside the
>> scope of fast
>>> BLAS routines, but "embarrassingly parallel" code could
easily be
>>> written for the relevant hardware. Even on uniprocessor
>> systems there
>>> might be speedups that the C compiler cannot find (e.g. because it
>>> cannot assume that source and destination of the operation are
>>> distinct).
>>
>> My guess is that for anything beyond basic operations we are
>> doing OK on uniprocessors. but it would be useful to do some
>> testing to be sure. For the basic operations I suspect we
>> are paying a heavy price for the way we handle recycling,
>> both in terms of overhead as such and in terms of inhibiting
>> compiler optimizations. For performance it would probably be
>> better to code the scalar-vector, equal-length-vector, and
>> general cases separately, though keeping the code
>> maintainable may be a bit of a challenge. Again testing on a
>> range of platforms and compilers would be useful.
>>
>> With multiprocessors likely to become more widely available
>> it would be good to look into ways of factoring the
>> vectorized math code so we can slide in one that uses threads
>> when approprate. This should dovetail nicely with
>> compilation to identify larger vectorized expressions that
>> can be parallelized as a unit; I hope to look into this a bit
>> this summer.
>>
>> luke
>>
>>
>>
>> --
>> Luke Tierney
>> Chair, Statistics and Actuarial Science
>> Ralph E. Wareham Professor of Mathematical Sciences
>> University of Iowa Phone: 319-335-3386
>> Department of Statistics and Fax: 319-335-3017
>> Actuarial Science
>> 241 Schaeffer Hall email: luke@stat.uiowa.edu
>> Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu
>>
>> ______________________________________________
>> R-help@stat.math.ethz.ch mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide!
>> http://www.R-project.org/posting-guide.html
>>
>
>
--
Luke Tierney
Chair, Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa Phone: 319-335-3386
Department of Statistics and Fax: 319-335-3017
Actuarial Science
241 Schaeffer Hall email: luke@stat.uiowa.edu
Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu