Hi Josh,
I think we need some more details, including code, and information
about your operating system. My machine has only 12 Gb of ram, but I
can run this quite comfortably (no swap, other processes using memory
etc.):
library(parallel)
library(data.table)
d <- data.table(a = rnorm(5000),
Hello,
I have been having issues using parallel::mclapply in a memory-efficient
way and would like some guidance. I am using a 40 core machine with 96 GB
of RAM. I've tried to run mclapply with 20, 30, and 40 mc.cores and it has
practically brought the machine to a standstill each time to the poin
I am trying to install two R packages to produce cartograms like in the work
of Gastner and Newman:
http://www.pnas.org/content/101/20/7499.full.pdf
but I have a problem installing Rcartogram and rdyncall packages.
Both are not available in CRAN and have to be installed from archivea and
produce
