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In this particular case, it looks to me like you're getting a significant
speedup after setting it to use 8 cores rather than 1 (elapsed time of 42 vs
72 seconds -- with threaded applications it's the wall clock that counts).
The fact that there is a big increase in the user (CPU) time indicates that
there is a lot of overhead for this problem in using multiple threads, but
nonetheless it's a net real-world benefit.

# David Smith

On Thu, Sep 24, 2009 at 7:13 AM, Jason Liao <JLIAO@hes.hmc.psu.edu> wrote:

> It runs more than twice as slowly using 8 core than using a single core
> in inverting large matrix. Tested on 8 core Windows XP 64 machine.
>
>
>
>
>
> > n = 1000
>
> > n.simu = 100
>
> > func1 = function()
>
> + {
>
> +   x = rnorm(n*n)
>
> +   dim(x)=c(n,n)
>
> +  y = solve(x)
>
> + }
>
> >
>
> > setMKLthreads(1)
>
> > system.time(for(i in 1:n.simu) func1())
>
>   user  system elapsed
>
>  69.48    2.42   71.91
>
> >
>
> > setMKLthreads(8)
>
> > system.time(for(i in 1:n.simu) func1())
>
>   user  system elapsed
>
>  179.06   17.90   41.70
>
>
>
> Jason Liao
>
>
>        [[alternative HTML version deleted]]
>
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>



-- 
David M Smith <david@revolution-computing.com>
Director of Community, REvolution Computing www.revolution-computing.com
Tel: +1 (206) 577-4778 x3203 (San Francisco, USA)

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