On May 10, 2010, at 3:44 PM, Stefan Evert wrote:
> Just a thought: Wouldn't it make more sense to compare the "elapsed" times,
> which show that both machines are more or less equally fast (with a slide
> edge for the newer i5)?
>
> I suspect that there is a change in the way "user" time is reported, which
> probably adds up running times of four hyperthreads running on two cores for
> the i5 CPU vs. only two threads on two cores for the Core 2 Duo. If I'm not
> mistaken about the i5 architecture, this is not surprising: there are 4
> threads, but they have to share 2 cores and don't seem to be able to run the
> FP instructions in parallel on a single core; so they're running at half
> speed only.
>
Oh my - how could I miss that :) Stefan, you're right - the elapsed is indeed
shorter on the i5 so ATLAS simply spreads some part across threads without any
gain (unsurprising on such small tasks). I got thrown off by the fact that
Shark was not reporting other threads, byt that may be an issue in Shark...
> Thanks for the benchmark, by the way. It's good to know I'm not missing out
> on R performance with my good old 2008 MacBook Pro. :-)
>
Well, ever since the GHz race is over the performance of machines has not
increased (or even to the contrary) since it's hard to feed all cores for
common R tasks in practice and per-thread speed has gone down (see the old
Nehalem benchmarks thread - unfortunately it's still true).
Cheers,
Simon
>
> On 8 May 2010, at 20:53, Gardar Johannesson wrote:
>
>> ###########################################
>> ## Results from new macbook pro (Core i5 @ 2.4Ghz)
>>
>>> set.seed(1)
>>> A <- matrix(rnorm(2000*2000),2000,2000)
>>> system.time(B <- crossprod(A))
>> user system elapsed
>> 2.500 0.058 0.816
>>> system.time(B <- crossprod(A))
>> user system elapsed
>> 2.502 0.050 0.814
>>> system.time(solve(B))
>> user system elapsed
>> 7.208 0.265 2.740
>>> system.time(solve(B))
>> user system elapsed
>> 7.121 0.264 2.666
>>> system.time({a <- rep(1.0,100); for(i in 1:1e6) a <- 1.0*a+0.0})
>> user system elapsed
>> 2.964 0.602 3.528
>>> system.time({a <- rep(1.0,100); for(i in 1:1e6) a <- 1.0*a+0.0})
>> user system elapsed
>> 3.040 0.732 3.732
>>
>
>> ###################################################
>> ## Results from old macbook pro (Core 2 Duo @ 2.2GHz)
>>
>>> set.seed(1)
>>> A <- matrix(rnorm(2000*2000),2000,2000)
>>> system.time(B <- crossprod(A))
>> user system elapsed
>> 1.429 0.073 0.800
>>> system.time(B <- crossprod(A))
>> user system elapsed
>> 1.429 0.064 0.874
>>> system.time(solve(B))
>> user system elapsed
>> 4.532 0.285 2.860
>>> system.time(solve(B))
>> user system elapsed
>> 4.521 0.281 2.834
>>> system.time({a <- rep(1.0,100); for(i in 1:1e6) a <- 1.0*a+0.0})
>> user system elapsed
>> 3.501 0.764 4.215
>>> system.time({a <- rep(1.0,100); for(i in 1:1e6) a <- 1.0*a+0.0})
>> user system elapsed
>> 3.459 0.702 4.113
>>> sessionInfo()
>> R version 2.11.0 (2010-04-22)
>> i386-apple-darwin9.8.0
>>
>> locale:
>> [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
>>
>> attached base packages:
>> [1] stats graphics grDevices utils datasets methods base
>>>
>>
>>
>> ###################################################
>> ## Results from new macbook pro (Core i5 @ 2.4Ghz)
>> ## Linking against Goto2 BLAS (vs vecLib)
>>
>>> set.seed(1)
>>> A <- matrix(rnorm(2000*2000),2000,2000)
>>> system.time(B <- crossprod(A))
>> user system elapsed
>> 2.348 0.124 0.635
>>> system.time(B <- crossprod(A))
>> user system elapsed
>> 2.342 0.110 0.622
>>> system.time(solve(B))
>> user system elapsed
>> 6.634 0.327 2.158
>>> system.time(solve(B))
>> user system elapsed
>> 6.697 0.348 2.034
>>> system.time({a <- rep(1.0,100); for(i in 1:1e6) a <- 1.0*a+0.0})
>> user system elapsed
>> 2.577 0.548 2.885
>>> system.time({a <- rep(1.0,100); for(i in 1:1e6) a <- 1.0*a+0.0})
>> user system elapsed
>> 2.411 0.478 2.859
>
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