When doing repeated regressions on large data sets, I'm finding that
the time spent on garbage collection often exceeds the time spent on
the regression itself. Consider this test program which I'm running
on an Intel Haswell i7-4470 processor under Linux 3.13 using R 3.1.2
compiled with ICPC 14.1:
nate@haswell:~$ cat > gc.R
library(speedglm)
createData <- function(n) {
int <- -5
x <- rnorm(n, 50, 7)
e <- rnorm(n, 0, 1)
y <- int + (1.2 * x) + e
return(data.frame(y, x))
}
gc.time()
data <- createData(500000)
data.y <- as.matrix(data[1])
data.x <- model.matrix(y ~ ., data)
for (i in 1:100) speedglm.wfit(X=data.x, y=data.y, family=gaussian())
gc.time()
nate@haswell:~$ time Rscript gc.R
Loading required package: Matrix
Loading required package: methods
[1] 0 0 0 0 0
[1] 10.410 0.024 10.441 0.000 0.000
real 0m17.167s
user 0m16.996s
sys 0m0.176s
The total execution time is 17 seconds, and the time spent on garbage
collection is almost 2/3 of that. My actual use case is a package
that creates an ensemble from a variety of cross-validated
regressions, and exhibits the same poor performance. Is this expected
behavior?
I've found that I can reduce the garbage collection time to a
tolerable level by setting the R_VSIZE environment value to a large
enough value:
nate@haswell:~$ time R_VSIZE=1GB Rscript gc.R
Loading required package: Matrix
Loading required package: methods
[1] 0 0 0 0 0
[1] 0.716 0.025 0.739 0.000 0.000
real 0m7.694s
user 0m7.388s
sys 0m0.309s
I can do slightly better with even higher values, and by using
R_GC_MEM_GROW=3. But while using the environment variables solves the
issue for me, I fear that the end users of my package won't be able to
set them. Is there a way that I can achieve the higher performance
from within R rather than from the command line?
Thanks!
--nate
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