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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] BLAS backend
- From: Christian Mayer <mail@xxxxxxxxxxxxxxxxx>
- Date: Fri, 16 Oct 2009 11:08:48 +0200
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Hi all,
@Jean Sreng: I don't know what your VR simulation is doing in detail.
But for the normal 3D stuff you should be using small (3x3 or 4x4) fixed
size matrices; those are perfectly supported by Eigen2 and using any
BLAS implementation will only give you worse performance.
If you are using big, variable sized matrices (e.g. to solve liear
equation systems) the question for a BLAS backend is valid although it's
worth a try to use the native implementation of Eigen2.
As Eigen2 is using expression templates even a GPU based backend might
profit by eigen as it can optimize the calculations to minimize the data
transfers between CPU and GPU do to late evaluation of the equation.
Aron Ahmadia schrieb:
> It actually turns out that one of my students this semester is
> considering how to parallelize Eigen using OpenMP for multithreading,
I'm not too fond of OpenMP for parallelisation in our case. It's very
good to parallelisate an loop (i.e. e.g. to multithread a single matrix
multiplication) but to take advantage of the expression templates it
might be not powerfull enough. MPI might be an better option there.
Parallelisation at the algorithm level (= expression template level)
gives you the advantage to perform operations that have no dependancy at
each other. For example:
result = A*B + C*D (A,B,C,D are big matrices)
It's much better to have - one a two core CPU - one thread that's
calculation A*B and another doing C*D than both threads fighting each
other (= locks) doing A*B and then doing an C*D...
In the end you need multhithreaded elementary operations (like GEMM)
*and* high level parallelisation of the algorithm. And an optimizer
that's deciding the best multithreading strategy for each algorithm. An
very interesting and highly complex task.
CU,
Christian
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