parallel SNOW slower than single core?

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I'm not surprised -- you're doing a fairly trivial calculation so the
overhead of setting up the parallelization is likely more than the
benefit from halving the computation time. Do it on a larger
calculation and I imagine the performance will be better -- I might
just be pulling this out of thin air, but I think I remember reading
slides from Luke Tierney saying one had to be adding vectors of size
= 30k or so for parallelization to have a meaningful benefit.
Obviously sizes will be lower for operations more expensive than
addition.

Michael
Dear All,

I am learning parallel in R and start with the package "snow". I did a test
about running time and the parallel version is much slower than the regulat
code. My laptop is X200s with dual core intel L9400 cpu.
Should I make more clusters than 2? Or how to improve the performance?

# install.packages("snow")
library(snow)
cl <- makeCluster(2)
t1 <- proc.time()
a <- c()
for (i in 1:1000)
{
a[i] <- sum(parSapply(cl, 1:15, get("+"), 2))
}
proc.time()-t1
t2 <- proc.time()
a <- c()
for (i in 1:1000)
{
a[i] <- sum(sapply(1:15, "+", 2))
}
proc.time()-t2

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Dear All,

I am learning parallel in R and start with the package "snow". I did a test
about running time and the parallel version is much slower than the regulat
code. My laptop is X200s with dual core intel L9400 cpu.
The OS matters far more.  Overheads on Windows are much higher than on 
Linux for example.
Should I make more clusters than 2? Or how to improve the performance?
Not use such small tasks.  This is discussed in the 'parallel' vignette 
(and we do suggest you start with 'parallel' these days).
# install.packages("snow")
library(snow)
cl <- makeCluster(2)
t1 <- proc.time()
a <- c()
for (i in 1:1000)
{
a[i] <- sum(parSapply(cl, 1:15, get("+"), 2))
}
proc.time()-t1
t2 <- proc.time()
See ?system.time
a <- c()
for (i in 1:1000)
{
a[i] <- sum(sapply(1:15, "+", 2))
}
proc.time()-t2

Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595