Dirk, I've run the code a couple times with valgrind, one time calling sparse 5
times, the other calling sparse 10 times.
Below is the main code and valgrind output.
What do you think, could it be an Armadillo / RcppArmadillo leak?
testMatrix <- Matrix(data=0,nrow=1e7,ncol=1e7,sparse=TRUE)
for(
Thanks Dirk and Hadley.
Dirk, asap I will try your b) and c) suggestions. While for suggestion a), I
didn't really understand how to implement it.
Hadley, I am determining rsession process memory looking at osx activity
monitor utility, under Real Mem column.
This is what I get (I am using your
> After every call, the memory from gc() is stable, while rsession process
> memory keeps growing of around 100Mb at each call and doesn't decrease after
> the garbage collection.
You don't mention how you're determining the size of the rsession
process, but you shouldn't necessarily expect it to
On 18 November 2013 at 12:41, Alessandro Vitale wrote:
| Hi,
|
| I was trying RcppArmadillo sparse matrix and found an odd memory behavior.
|
| I used this code from Romain http://stackoverflow.com/questions/18336021/
|
sparse-matrix-conversion-from-matrix-to-armadillo-with-rcpparmadilloextensi
Hi,
I was trying RcppArmadillo sparse matrix and found an odd memory behavior.
I used this code from Romain
http://stackoverflow.com/questions/18336021/sparse-matrix-conversion-from-matrix-to-armadillo-with-rcpparmadilloextensions-s
and added the first line to manage large matrices
#define AR
