Dear R developers,
with the inclusion of the package parallel in the upcoming release of R,
users and package developers are likely to make increasing usage of
parallelization features. In part, these features rely on forking the R
process. As ?mcfork points out, fork()ing in a GUI process is
Following helpful correspondence with Mark Bravington, mvbutils::foodweb and
callers.of can do exactly what I wanted very neatly and easily.
The trick is to use base::asNamespace to see non-exported objects in the
package. base::asNamespace is described as Internal name space support
On Oct 4, 2011, at 4:43 AM, Thomas Friedrichsmeier wrote:
Dear R developers,
with the inclusion of the package parallel in the upcoming release of R,
users and package developers are likely to make increasing usage of
parallelization features. In part, these features rely on forking the
My thanks to Bill Dunlap and Simon Urbanek for clarifying many of the
details. This gives me what I need to go forward.
Yes, I will likely convert more and more things to .Call over time.
This clearly gives the most control over excess memory copies. I am
getting more comments from people
Hi,
On Tuesday 04 October 2011, Simon Urbanek wrote:
I don't see why this should be anything new - this is already happening
since both packages that were folded into parallel (snow and multicore)
are well known and well used.
In multicore we were explicitly warning about this and also
Hi there,
I think I'm encountering a bug, and I already reported it here:
https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=14695
But meanwhile, could you help me by any suggestions about the problem?
I'll place the content of the reported bug here. You can find
attachments on bugzilla if
The bug is in your code! (I see at least one - many buffer overflows in all
char** output arguments). Please don't abuse the bug tracking system for usage
questions.
You may want to consider using either .Call (if you are familiar with R) or
Rcpp (if you are more familiar with C++), .C is not
Allocating many small objects triggers numerous garbage collections as R
grows its memory, seriously degrading performance. The specific use case
is in creating a STRSXP of several 1,000,000's of elements of 60-100
characters each; a simplified illustration understating the effects
(because
