Re: [eigen] slow adjoint sparse dense product |

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*To*: eigen@xxxxxxxxxxxxxxxxxxx*Subject*: Re: [eigen] slow adjoint sparse dense product*From*: Gael Guennebaud <gael.guennebaud@xxxxxxxxx>*Date*: Fri, 11 Nov 2011 06:06:16 +0100*Dkim-signature*: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=gamma; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :content-type; bh=omvJ1Fr58QAsS6kGmbA1mwxebWGAxgIri6W25EM2G+Y=; b=LmDrtYspANxaYcFjInPE4gdUo9Y98ffgEHDRJCokPA+A0yFuFD1h+S0yGVPMhfBkFV /kmrqqj7GX1p/YeJZ7KRAABfFORyrxV6F2b8cDfGh62Q15pbDxzwAgBtAp/suzlgbyQy APQ3CMdatbpNoa7As1VLfhfiWDcCU0tCF+6qE=

Fixed in the default branch. The real problem was the cost of the scaling factor which was applied nnz times. gael On Fri, Nov 11, 2011 at 5:38 AM, Gael Guennebaud <gael.guennebaud@xxxxxxxxx> wrote: > OK, it seems that this issue does not always occur and hardly depends > on the compiler version and whether you use complexes or simply > doubles... So sure this have to be fixed. These products will also be > parallelized. Of course if your solver is already parallel then you > don't care! > > gael. > > On Thu, Nov 10, 2011 at 1:21 PM, Sebastian Birk > <birk@xxxxxxxxxxxxxxxxxxxxx> wrote: >> Hi, >> >> I am working on iterative solvers for large sparse matrices and I am >> thinking about switching from uBLAS to Eigen. But the one thing that >> keeps me from changing the library is that the product of the adjoint of >> a sparse matrix with a vector is about half the speed of multiplying >> directly with the non-adjoint matrix. I attached a small simple test >> file that measures the time for an example matrix. >> >> I figured out that for the product y=Ax in class SparseTimeDenseProduct >> the case "if(Rhs::ColsAtCompileTime==1)" is chosen and the computation >> is quite fast. >> But for y=A^{H}x there is no optimized case that can be chosen. Is there >> a way to speed up the multiplication with an adjoint sparse matrix? >> >> Sebastian >> >

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