R devel - Mar 2012 - parApply vs parCapply

I've started to use the parallel package and it works very well speeding
things up.  Thank you for making this easy to do.

Should I have expected that parCapply would return a vector
when parApply returns a matrix?

library(parallel)

x <- matrix(rnorm(8), nc = 2)
apply(x, 2, function(y) y)

            [,1]       [,2]
[1,] -0.9649685 0.91339851
[2,] -1.4313140 0.13457671
[3,]  1.0499248 1.58967879
[4,] -1.8974411 0.03639876

cl <- makeCluster(getOption("cl.cores", detectCores()))
parApply(cl, x, 2, function(y) y)

            [,1]       [,2]
[1,] -0.9649685 0.91339851
[2,] -1.4313140 0.13457671
[3,]  1.0499248 1.58967879
[4,] -1.8974411 0.03639876

parCapply(cl, x, function(y) y)

[1] -0.96496852 -1.43131396  1.04992479 -1.89744113  0.91339851  0.13457671
[7]  1.58967879  0.03639876

stopCluster(cl)
> sessionInfo()
R version 2.15.0 beta (2012-03-15 r58760)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods
[8] base

Thank you.

-- 
Ken Knoblauch
Inserm U846
Stem-cell and Brain Research Institute
Department of Integrative Neurosciences
18 avenue du Doyen L?pine
69500 Bron
France
tel: +33 (0)4 72 91 34 77
fax: +33 (0)4 72 91 34 61
portable: +33 (0)6 84 10 64 10
http://www.sbri.fr/members/kenneth-knoblauch.html

On 17/03/2012 17:03, Ken Knoblauch wrote:
> I've started to use the parallel package and it works very well
speeding
> things up. Thank you for making this easy to do.
>
> Should I have expected that parCapply would return a vector
> when parApply returns a matrix?
Maybe: I would have expected so, and AFAICS ?parCapply does not say what 
the value is so it is unspecified.  We probably should document it.
> library(parallel)
>
> x <- matrix(rnorm(8), nc = 2)
> apply(x, 2, function(y) y)
>
> [,1] [,2]
> [1,] -0.9649685 0.91339851
> [2,] -1.4313140 0.13457671
> [3,] 1.0499248 1.58967879
> [4,] -1.8974411 0.03639876
>
> cl <- makeCluster(getOption("cl.cores", detectCores()))
> parApply(cl, x, 2, function(y) y)
>
> [,1] [,2]
> [1,] -0.9649685 0.91339851
> [2,] -1.4313140 0.13457671
> [3,] 1.0499248 1.58967879
> [4,] -1.8974411 0.03639876
>
> parCapply(cl, x, function(y) y)
>
> [1] -0.96496852 -1.43131396 1.04992479 -1.89744113 0.91339851 0.13457671
> [7] 1.58967879 0.03639876
>
> stopCluster(cl)
>
>> sessionInfo()
> R version 2.15.0 beta (2012-03-15 r58760)
> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>
> locale:
> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>
> attached base packages:
> [1] parallel stats graphics grDevices utils datasets methods
> [8] base
>
> Thank you.
>

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595