multicore performance question

Thanks Rainer for your feedback.

What I got for the second case (also almost the same for a few trials):

nr <- 5000; X <- matrix(rnorm(1000), ncol=30, nrow=nr); set.seed(100)

system.time(unlist(lapply(1:nr, FUN=fn1, x=X))) #   user  system elapsed #  0.139   0.000
0.140


set.seed(100); system.time(unlist(mclapply(1:nr, FUN=fn1, x=X)))

#   user  system elapsed #  0.083   0.091   0.118


In that case mclapply is slightly faster than lapply. Do you have any idea why?

I'm running on an 8 cores machine with RHEL6 (64).

Thanks in advance,

Mauricio


On 09/07/12 12:03, Rainer M Krug wrote:

On 09/07/12 10:49, Mauricio Zambrano-Bigiarini wrote:

Dear list,

I need to evaluate a vectorial function (~30 dimensions), and I would like to know if it
is possible to take advantage of a multicore machine.

So far, I'm using the multicore package, and I'm not getting any time saving. I guess
the larger the computation time the larger the gain in performance, and probably that is
the reason why I'm not speeding up my computations by using multicore.

However, I would like to ask you if the reason for not getting any time saving by using 
'multicore'

Well - I see a difference:

Without multicore: user  system elapsed 0.020   0.000   0.023

With multicore: user  system elapsed 0.012   0.024   0.048

Approximately the same for a few tries, and the two times factor one would expect (I use a
dual core).

Or even more pronounced:

nr<- 5000; X<- matrix(rnorm(1000), ncol=30, nrow=nr) set.seed(100); 
system.time(unlist(lapply(1:nr, FUN=fn1, x=X)))

user  system elapsed 0.224   0.004   0.229

set.seed(100); system.time(unlist(mclapply(1:nr, FUN=fn1, x=X)))

user  system elapsed 0.120   0.044   0.174

Cheers,

Rainer

is the short time of my computations OR  the way in which I defined the function to be
run (fn and fn1):

----------- START -------------- library(multicore)

fn<- function(x) { n<- length(x) return(1 + (1/4000) * sum(x^2) - 
prod(cos(x/sqrt(seq(1:n))))) }

fn1<- function(i, x) fn(x[i,])

nr<- 500 X<- matrix(rnorm(1000), ncol=30, nrow=nr)

# No multicore set.seed(100) system.time( unlist(lapply(1:nr, FUN=fn1, x=X)) )

# multicore set.seed(100) system.time( unlist(mclapply(1:nr, FUN=fn1, x=X, mc.cores=6))
)

sessionInfo() R version 2.15.0 (2012-03-30) Platform: x86_64-redhat-linux-gnu (64-bit)

locale: [1] LC_CTYPE=en_GB.utf8       LC_NUMERIC=C [3] LC_TIME=en_GB.utf8 
LC_COLLATE=en_GB.utf8 [5] LC_MONETARY=en_GB.utf8    LC_MESSAGES=en_GB.utf8 [7]
LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C              LC_TELEPHONE=C [11]
LC_MEASUREMENT=en_GB.utf8 LC_IDENTIFICATION=C

attached base packages: [1] stats     graphics  grDevices utils     datasets  methods
base

other attached packages: [1] multicore_0.1-7  hydroPSO_0.1-57  hydroTSM_0.3-5-2 xts_0.8-6
[5] zoo_1.7-7        sp_0.9-99

loaded via a namespace (and not attached): [1] automap_1.0-12 class_7.3-4
cluster_1.14.2 e1071_1.6 grid_2.15.0 [6] gstat_1.0-12   Hmisc_3.9-3    lattice_0.20-6
tools_2.15.0

----------- END --------------


any comment will be very much appreciated.


Thanks in advance,


Mauricio Zambrano-Bigiarini

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