Ashish Kulkarni wrote:
> Hin-Tak Leung wrote:
>
>> It might be interesting to know get some details on your hardware.
>>
>>
>
> It's a P4 2.66GHz with a standard Intel motherboard having 1GB RAM.
>
>
>> On my box, linux native seems to be a little slower than
>> your quick.serialize times:
>>
>> > system.time( serialize(matrix(0, 1000, 1000), NULL) )
>> [1] 0.372 0.288 0.692 0.000 0.000
>> > system.time( serialize(matrix(0, 2000, 2000), NULL) )
>> [1] 1.237 1.195 2.501 0.000 0.000
>>
>> running R 2.4.1 windows under wine (same box) is a good deal s
>> lower, but is not anywhere nearly as slow as yours.
>>
>> > system.time( serialize(matrix(0, 1000, 1000), NULL) )
>> [1] 0.00 0.00 6.08 NA NA
>> > system.time( serialize(matrix(0, 2000, 2000), NULL) )
>> [1] 0.01 0.01 78.00 NA NA
>> >
>>
>>
>
> Well, the timings certainly differ quite a bit. What kind of
> hardware do you have? I suspect that most of the delay may be
> caused by memory reallocation since the size of the output
> raw array is not known up front. I would imagine that WINE
> would use the system memory allocator, not the one used by
> the windows kernel.
>
>
>> Since you mentioned that you are using Rmpi, there is a possibility
>> that you might be calling a different serialize() than base::serialize
>> all together???
>>
>
> Nope, I hit this problem while using Rmpi and tracked it down
> to .mpi.serialize, which in turn calls base::serialize. From Rmpi:
>
> .mpi.serialize <- function (obj)
> {
> trans_obj = serialize(obj, NULL)
> if (getRversion() >= "2.4.0")
> return(trans_obj)
> else return(charToRaw(trans_obj))
> }
>
>
Just a little sanity check on Linux. This seems to indicate an
essentially constant run time per million matrix elements. So I suspect
that something Windows-specific is going on.
> x <- sapply(1:8, function(i) {print(i); system.time(
serialize(matrix(0, i*1000, i*1000), NULL) )})
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
> matplot(t(x)/(1:8)^2)
> t(x)
[,1] [,2] [,3] [,4] [,5]
[1,] 0.292 0.660 0.952 0 0
[2,] 1.368 2.492 3.861 0 0
[3,] 2.853 5.861 8.713 0 0
[4,] 5.016 10.540 15.555 0 0
[5,] 8.136 16.425 24.875 0 0
[6,] 11.481 23.926 35.427 0 0
[7,] 15.729 32.810 48.544 0 0
[8,] 20.998 43.218 64.839 0 0
--
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