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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] FLENS C++ expression template Library has excellent documentation
- From: Christian Mayer <mail@xxxxxxxxxxxxxxxxx>
- Date: Sat, 18 Apr 2009 17:43:54 +0200
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Rohit Garg schrieb:
>> So, in which area does Intel MKL still have a long-term lead? I would
>> say parallelization. We haven't started that yet and it is probably a
>> very, very tough one. It's what I have in mind when I say that a
>> BLAS/LAPACK wrapper is still welcome.
>
> Why do you think parallelization is very difficult? Do you mean
> parallelization nfrastructure? AFAICS, using openmp will be cool. Let
> compiler handle all the dirty buisness etc This is something I want to
> explore (time availability is of course important !) so I would like
> some heads up.
(disclaimer: I haven't done much parallelization in the past, but I
listened with much interest a few lectures and different discussions on
the net)
Do NOT use OpenMP in our case!
OpenMP is great to parallelize a few loops in old code where you can't
spend the time to do it right. If you've got the choice you should
allways rethink every algorithm and implement it in a parallel way (with
the threading lib of your choice)
Why?
You've got much more control and you can think ahead.
In the case of EIGEN with the expression templates we have a very strong
base that can support such an approach. As the compiler knows the
calculations ahead it could parallelize some calcutations. E.g. look at
the expression:
E = A*B + C*D
You could do it the dumb way to use a parallel A*B, then a parallel C*D
and at the end a parallel E = prod1 + prod2. That's what OpenMP could
offer you.
But wouldn't it be much wiser to run A*B in one thread, C*D in another
and a E = prod1 + prod2? That's much better with data location...
But chaning that code to use a very lightweight sceduler and different
tasks with minimal locking and still best data locality is hard work.
But it'll be woth it - for the big matrix case (I *guess* small, fixed
matrices won't benefit at the EIGEN level of parallelsation. There the
bigger algorithms have to take care of it).
CU,
Chris
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