|Re: [eigen] A not so simple product|
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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] A not so simple product
- From: "Benoit Jacob" <jacob.benoit.1@xxxxxxxxx>
- Date: Tue, 9 Dec 2008 15:17:23 +0100
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Indeed, the "obvious" way
for(i = 0; i < N; i++) m.row(i) /= rho(i)*w(i);
doesn't vectorize because rows have size 3... so if you want
vectorization you need a complicated trick as Gael shows.
Gael: in order to achieve vectorization of diagonal products, wouldn't
it be simplest to just change DiagonalProduct.h so it takes in account
the fact that coefficients come from a vector?
Something like replacing m_lhs.coeff(row, row) by
m_lhs.m_coeffs.coeff(row), since we know that m_lhs is a
2008/12/9 Gael Guennebaud <gael.guennebaud@xxxxxxxxx>:
> I guess there is no M and you wanted to write "VectorXf w(N);" instead of
> "VectorXf w(M);" ?
> Anyway, you can write it as a single expression like that:
> m = ( (rho.cwise()*w).cwise().inverse().asDiagonal() ) * m;
> the matrix-matrix product will be optimized since Eigen knows that the
> left-hand side (lhs) matrix is a diagonal matrix. Unfortunately, Eigen does
> not take into account yet the fact that the diagonal of lhs is actually
> stored as a vector, and therefore this expression won't be vectorized.
> The good think is that this is an optimization we can quite easily add. So I
> would recommand to do so and wait a bit that such expressions get vectorized
> by Eigen.
> Note that in the meantime, if performance matters you can also do:
> rho (or w, or a temporary) = rho.cwise()*w).cwise().inverse();
> for (int k=0: k<3; ++k)
> m.col(i).cwise() *= rho (or w, or a temporary);
> in which case all computations will be vectorized at the cost of additionnal
> read/writes operations.
> Below are some proposals related to that problem:
> Another option would be to add a mechanism to extend a vector to a matrix
> with repetitions:
> m = m .cwise()* ( (rho.cwise()*w).cwise().inverse().duplicate(3) );
> I'm not sure about "duplicate" for the name but I know matlab offers such a
> feature. For the vectorization this is a bit tricky because such a
> "Duplicate" expression can be vectorized in both direction: you can either
> return column packets of row packets (by repeating a scalar in each
> component of the packet) to accomodate the storage of m. However, this fine
> tunning of the packet generation is something we cannot support without big
> changes in Eigen (or ugly workarounds).
> Finally, a third solution to implement this kind of thing could be to extend
> the rowwise/colwise API:
> m = m.colwise() * (rho.cwise()*w).cwise().inverse();
> perhaps this last proposal is a bit confusing.
> In any case the choice for the traversal of m is a bit tricky. Assuming m is
> column major, we can process it column per column that is good wrt to the
> cache of m but which requires a temporary to store
> "rho.cwise()*w).cwise().inverse()", or we process m per block of packet-size
> On Tue, Dec 9, 2008 at 2:10 PM, Benjamin Schindler
> <bschindler@xxxxxxxxxxxxxxx> wrote:
>> Okay, I'm trying to figure the most efficient way to vectorize this
>> MatrixXf m(N,3);
>> VectorXf rho(N);
>> VectorXf w(M);
>> What I would like to do: The i'th row of m is suppposed to get multiplied
>> by 1 / (rho(i)*w(i))
>> I've not yet found a way which actually works as a short
>> eigen-expression... is there any or do I have to go back to the old rusty
>> for loop? :)