[R-SIG-Mac] [R-sig-hpc] Grand Central Dispatch (simple loop optimization)
Jan de Leeuw
deleeuw at stat.ucla.edu
Thu Sep 17 22:16:58 CEST 2009
on my system (2 x 2.93 quad core Nehalem
with hyper-threading, so 16 threads max, 16GB RAM,
10.6.1, 64bit kernel, 64bit R)
> system.time(threads(100000,1000,"omp"))
user system elapsed
10.249 0.009 0.662
> system.time(threads(100000,1000,"gcd"))
user system elapsed
10.208 0.008 0.668
> system.time(threads(100000,1000,"dcg"))
user system elapsed
8.731 0.005 8.738
so omp == gcd, but for more complicated tasks the
tighter integration may favor gcd
comparing harpertown and nehalem --> surprising
difference (kernel ? hyper-threading ?)
i have no idea how the open-sourced gcd works on
non-mac hardware
code is downloadable using webdav from
public.me.com/jdeleeuw/software/threads
or using afp://gifi.stat.ucla.edu from
the deleeuw public directory
On Sep 17, 2009, at 12:35 , Simon Urbanek wrote:
> On Sep 17, 2009, at 15:20 , Simon Urbanek wrote:
>
>> Jan,
>>
>> thanks for sharing this. This is really interesting. We have been
>> contemplating using GCD for R (mainly pnmath) but at the time OMP
>> was faster. However, GCD got apparently really good in the meantime:
>>
>> > system.time(threads(100000,1000,"omp_try"))
>> user system elapsed
>> 9.671 0.009 2.441
>> > system.time(threads(100000,1000,"gcd_try"))
>> user system elapsed
>> 9.592 0.004 2.410
>> > system.time(threads(100000,1000,"dcg_try"))
>> user system elapsed
>> 9.784 0.003 9.788
>>
>> [This is on Harpertown 2.66GHz quad core]
>>
>> So GCD is surprisingly just a hair faster than OMP (also surprising
>> to me is that using more threads than cores make OMP faster - the
>> above is with 16 threads).
>>
>
> Actually, with schedule(dynamic) the gap is almost at the level of
> the measurement error:
>
> > system.time(threads(100000,1000,"omp_try"))
> user system elapsed
> 9.614 0.006 2.420
> > system.time(threads(100000,1000,"gcd_try"))
> user system elapsed
> 9.586 0.005 2.409
>
> -- the OMP line (to be placed before the for() loop) is#pragma omp
> parallel for default(shared) private(i) schedule(dynamic)
>
> Cheers,
> Simon
>
>
>>
>> On Sep 17, 2009, at 14:24 , Jan de Leeuw wrote:
>>
>>> a) Obviously OpenMP is more portable. Even on a Mac I had to use
>>> Apple's gcc in this case
>>> (I normally use the GNU gcc-trunk).
>>>
>>> b) GCD does not require specifying the number of threads -- it
>>> determines it at runtime.
>>>
>>> c) Coding is simpler.
>>>
>>
>> I would not say - OMP takes just one #pragma - no need to change
>> your code whereas GCD requires several special function calls...
>> However, OMP is more limited in the kind of things you can do.
>>
>> Cheers,
>> Simon
>>
>>
>>> d) Since GCD is at a lower OS level than OpenMP, it will probably
>>> handle resource allocation
>>> better. But my small example, on an otherwise idle Mac Pro (16
>>> cores, 32 GB of RAM), does
>>> not really highlight that.
>>>
>>> e) For more info, and some OpenMP comparisons, see
>>>
>>> http://www.macresearch.org/cocoa-scientists-xxxi-all-aboard-grand-central
>>> http://arstechnica.com/apple/reviews/2009/08/mac-os-x-10-6.ars/12
>>>
>>> To quote Syracuse
>>>
>>> "Write your application as usual, but if there's any part of its
>>> operation that can
>>> reasonably be expected to take more than a few seconds to
>>> complete, then for the love of Zarzycki,
>>> get it off the main thread!"
>>>
>>> On Sep 17, 2009, at 11:03 , Saptarshi Guha wrote:
>>>
>>>> Nice, how does this compare when using OpenMP?
>>>> How does it compare when several other core hungry processes are
>>>> running?( GC is supposed to nicely handle resource allocation,
>>>> does OpenMP compete with the other processes?).
>>>>
>>>> Regards
>>>> Saptarshi
>>>>
>>>>
>>>
>>> ===
>>> Jan de Leeuw; Distinguished Professor and Chair, UCLA Department
>>> of Statistics;
>>> Director: UCLA Center for Environmental Statistics (CES);
>>> Editor: Journal of Multivariate Analysis, Journal of Statistical
>>> Software;
>>> US mail: 8125 Math Sciences Bldg, Box 951554, Los Angeles, CA
>>> 90095-1554
>>> phone (310)-825-9550; fax (310)-206-5658; email: deleeuw at stat.ucla.edu
>>> .mac: jdeleeuw ++++++ aim: deleeuwjan ++++++ skype: j_deleeuw
>>> homepages: http://gifi.stat.ucla.edu ++++++ http://www.cuddyvalley.org
>>> -------------------------------------------------------------------------------------------------
>>> No matter where you go, there you are. --- Buckaroo Banzai
>>> http://gifi.stat.ucla.edu/sounds/nomatter.au
>>>
>>> _______________________________________________
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>>
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>
>
===
Jan de Leeuw; Distinguished Professor and Chair, UCLA Department of
Statistics;
Director: UCLA Center for Environmental Statistics (CES);
Editor: Journal of Multivariate Analysis, Journal of Statistical
Software;
US mail: 8125 Math Sciences Bldg, Box 951554, Los Angeles, CA 90095-1554
phone (310)-825-9550; fax (310)-206-5658; email: deleeuw at stat.ucla.edu
.mac: jdeleeuw ++++++ aim: deleeuwjan ++++++ skype: j_deleeuw
homepages: http://gifi.stat.ucla.edu ++++++ http://www.cuddyvalley.org
-------------------------------------------------------------------------------------------------
No matter where you go, there you are. --- Buckaroo Banzai
http://gifi.stat.ucla.edu/sounds/nomatter.au
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