On Fri, Nov 16, 2012 at 8:42 PM, Ryan C. Thompson wrote:
> The difference is that in the parallel package, you use mclapply for
> multicore and parLapply for multi-machine parallelism. If you want to
> switch from one to the other, you have to change all your code that uses
> either function to th
Actually, my previous post had a small bug in it: it would throw an
error when printing a zero-column data frame. The following code fixes this:
print.data.frame <- function(df) {
if (ncol(df) > 0 && require("IRanges")) {
prev.max.print <- getOption("max.print")
on.exit(options(max.pri
Hi all,
I noticed that DataFrame objects have a much faster and more practical
printing format than base R's data.frame class. So I wrote a replacement
for "print.data.frame" that prints data.frames in the same style as
DataFrames. Just stick it in your ~/.Rprofile and your data.frames will
m
On 11/17/2012 02:39 AM, Ramon Diaz-Uriarte wrote:
In addition to Steve's comment, is it really a good thing that "all code
stays the same."? I mean, multiple machines vs. multiple cores are,
often, _very_ different things: for instance, shared vs. distributed
memory, communication overhead diff
In reply to:
On 11/16/2012 09:45 PM, Steve Lianoglou wrote:
But then you have the situation of multi-machines w/ multiple cores --
is this (2) or (3) here? How do you explicitly write code for that w/
foreach mojo? I guess the answer to that is that you let your "grid
engine" (or whatever your
On Sat, 17 Nov 2012 00:45:46 -0500,Steve Lianoglou
wrote:
> Ding!
> On Fri, Nov 16, 2012 at 11:42 PM, Ryan C. Thompson
> wrote:
> > The difference is that in the parallel package, you use mclapply for
> > multicore and parLapply for multi-machine parallelism. If you want to switch
> > from
