[R] Memory hungry routines

Duncan Murdoch murdoch.duncan at gmail.com
Mon Dec 29 20:32:50 CET 2014

> Is there any way to detect which calls are consuming memory?

The Rprofmem() function can do this, but you need to build R to enable
it.    Rprof() does a more limited version of the same thing if run with
memory.profiling = TRUE.

Duncan Murdoch

> I run a program whose global variables take up about 50 Megabytes of
> memory, but when I monitor the progress of the program it seems to
> allocating 150 Megabytes of memory, with peaks of up to 2 Gigabytes.
> I know that the global variables aren't "copied" many times by the
> routines, but I suspect something weird must be happening.
> Alberto Monteiro
> PS: the lines, below, count the memory allocated to all global
> variables, probably it could be adapted to track the local variables:
> y <- ls(pat="")   # get all names of the variables
> z <- rep(0, length(y))  # create array of sizes
> for (i in 1:length(y)) z[i] <- object.size(get(y[i]))  # loop: get all
> sizes (in bytes) of the variables
> # BTW, is there any way to vectorialize the above loop?
> xix <- sort.int(z, index.return = TRUE)  # sort the sizes
> y <- y[xix$ix]  # apply the sort to the variables
> z <- z[xix$ix]  # apply the sort to the sizes
> y <- c(y, "total")  # add a totalizator
> z <- c(z, sum(z))  # sum them all
> cbind(y, z)  # ugly way to list them
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