R devel - Oct 2012 - Problems with install.packages when Ncpus > 1

I have a Ubuntu Linux 12.04.1 machine running R 2.15.1. I'm trying to
run tests building packages, so install.packages() is trying to
install about about 1000 packages from source. I'm running into some
strange issues that seem related to using values for Ncpus other than 1.

When I use Ncpus=32, about 260 of the packages fail to install. When I
tried again with Ncpus=2, more of them get installed, and there were
only 234 failed packages. I tried it again with Ncpus=1, after which
there were only 142 failed packages. (I think these remaining failed
packages are due to missing external dependencies, like RODBC, Java,
and various development libraries, so these aren't a concern at the
moment.)

In the code for install.packages, I see that if Ncpus>1, it passes the
Ncpus to make, as in 'make -k -j 32'. Is it possible that these
packages are failing because of this option to make?

Has anyone else run into this issue before?

Thanks!
-Winston

On 06/10/2012 05:42, Winston Chang wrote:
> I have a Ubuntu Linux 12.04.1 machine running R 2.15.1. I'm trying to
> run tests building packages, so install.packages() is trying to
> install about about 1000 packages from source. I'm running into some
> strange issues that seem related to using values for Ncpus other than 1.
>
> When I use Ncpus=32, about 260 of the packages fail to install. When I
> tried again with Ncpus=2, more of them get installed, and there were
> only 234 failed packages. I tried it again with Ncpus=1, after which
> there were only 142 failed packages. (I think these remaining failed
> packages are due to missing external dependencies, like RODBC, Java,
> and various development libraries, so these aren't a concern at the
> moment.)
>
> In the code for install.packages, I see that if Ncpus>1, it passes the
> Ncpus to make, as in 'make -k -j 32'. Is it possible that these
> packages are failing because of this option to make?
It is perfectly correct option: please do your homework before posting 
as the posting guide asked of you.
>
> Has anyone else run into this issue before?
Yes.  It is due to missing dependencies between packages.   I find this 
works well on CRAN *provided* that the BioC repositories are also 
selected so that dependencies can be traced via BioC dependencies. 
However, BioC does have a fair few missing dependencies in its packages 
(run R CMD check over BioC to see them).

Take a look at the logs of the failed packages.  One I see frequently is 
that ddgraph fails because it depends indirectly on RBGL which takes a 
long time to install and is still being done.
>
> Thanks!
> -Winston

-- 
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