[R] enableJIT(2) causes major slow-up in rpart

[Uwe Ligges]

On 14.04.2012 09:01, Tal Galili wrote:
> Hello deari Uwe,
>
> What you explain about enableJIT makes sense, except that I would not
> expect it to slow down the function in an order of magnitude.
> If it was only adding a constant time to the startup time, I would
> understand, but I suspect that this is not the case here.  For example, see
> this code:
>
> enableJIT(0) #  making sure that JIT is off
> #[1] 3
> fo<- function() {for(i in 1:100) {rpart(Kyphosis ~ Age + Number + Start,
> data=kyphosis)}}
> system.time(fo())
> #   user  system elapsed
> #   0.70    0.02    0.78
> enableJIT(3)
> # [1] 0
> system.time(fo())
> #   user  system elapsed
> #  10.31    0.00   10.36
>
> If I understand enableJIT, it shoudl compile the functions inside fo and
> rpart once (hence the slower startup time), but once that is done, there
> should not be an extra overhead.  Am I missing something here?

I see, that's indeed suprising. I see Luke as the author of the code is 
going to look at it.

In any case, note that rpart is already byte compiled before you start 
your evaluations: All base and recommended packages are byte compiled by 
default nowadays.

Best,
Uwe



> With regards,
> Tal
>
>
>
>
>
>
> ----------------Contact
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>
> On Fri, Apr 13, 2012 at 12:47 PM, Uwe Ligges<
> ligges at statistik.tu-dortmund.de>  wrote:
>
>>
>>
>> On 12.04.2012 23:15, Tal Galili wrote:
>>
>>> Hello,
>>>
>>> Due to exploration of the JIT capabilities offered through the {compiler}
>>> package, I came by the fact that using enableJIT(2) can *slow* the rpart
>>>
>>> function (from the {rpart} package) by a magnitude of about 10 times.
>>>
>>> Here is an example code to run:
>>>
>>> library(rpart)
>>> require(compiler)
>>>
>>> enableJIT(0) # just making sure that JIT is off # We could also use
>>> enableJIT(1) and it would be fine
>>> fo<- function() {rpart(Kyphosis ~ Age + Number + Start, data=kyphosis)}
>>> system.time(fo())
>>> #   user  system elapsed
>>> #      0       0       0   # this can also be 0.01 sometimes.
>>>
>>> enableJIT(2)  # also happens for enableJIT(3)
>>> system.time(fo())
>>> #   user  system elapsed
>>> #   0.12    0.00    0.12
>>>
>>>
>> Because the overhead for JIT is considerable for this example and the time
>> critical parts of rpart are written in compiled code already.
>>
>> JIT compilers are good for inefficiently written R code such as loops that
>> could be avoided by vectorized operations. JIT cannot optimize runtime for
>> code already written in C (or written in excellent R code).
>>
>> Uwe Ligges
>>
>>
>>
>>
>>
>>
>>
>>> Which brings me to my *questions*:
>>>
>>> 1) Is this a bug or a feature?
>>> 2) If this is a feature, what is causing it? (or put another way, can one
>>> predict ahead of time the implications of using enableJIT(2) or
>>> enableJIT(3) on his code?)
>>>
>>>
>>> *Links*:
>>>
>>> A post I recently wrote about my exploration of JIT -
>>> www.r-statistics.com/2012/04/**speed-up-your-r-code-using-a-**
>>> just-in-time-jit-compiler/<http://www.r-statistics.com/2012/04/speed-up-your-r-code-using-a-just-in-time-jit-compiler/>
>>> The question asked on SO regarding the limitations of JIT:
>>> http://stackoverflow.com/**questions/10106736/possible-**
>>> shortcomings-for-using-jit-**with-r<http://stackoverflow.com/questions/10106736/possible-shortcomings-for-using-jit-with-r>
>>>
>>> Thanks,
>>> Tal
>>>
>>>
>>>
>>> ----------------Contact
>>> Details:----------------------**------------------------------**---
>>> Contact me: Tal.Galili at gmail.com |  972-52-7275845
>>> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
>>> www.r-statistics.com (English)
>>> ------------------------------**------------------------------**
>>> ------------------------------**----
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>>
>
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