Re: [eigen] Matrix decompositions |

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*To*: eigen <eigen@xxxxxxxxxxxxxxxxxxx>*Subject*: Re: [eigen] Matrix decompositions*From*: Sameer Agarwal <sameeragarwal@xxxxxxxxxx>*Date*: Thu, 28 Feb 2013 13:47:41 -0800*Dkim-signature*: v=1; a=rsa-sha256; c=relaxed/relaxed; d=google.com; s=20120113; h=mime-version:x-received:in-reply-to:references:date:message-id :subject:from:to:content-type; bh=tT3JRO6e0aasEjtuMpr5+R9pB8IEJ7Y4jZpQfqHomAM=; b=Kzc8lNLlZhEBq/Cs5r7lugTpqcXyxGUViPVKUQKocjTvOML3tJTKw5LUr1YyROPDfB RZGLYcvK+1b2PrML6GkBJc2jMOwDa45xvJD7BeSqIdpuizwdAWlhIVZl5uWbkMcFyFXg 0B3lKCDyaT2ijVIlnmeyl6FMEvYandHxwALdZYGJ+uQ/TQpJBAIA7vTeZUe/CMtzMOgL BLKAHG8pWcTlM7r387X2DJB3HtLdL/ieCGalXPp1qUQB5EO7hkVssUAbu6XpvKaHnkJz IVChQHhAXFaofwrTEsRZaBrBQIgW0ucSgxrfa/4tyQ+ocqFTpJngFv+paDjHIqE7qEYv UCpg==

On Thu, Feb 28, 2013 at 1:24 PM, Gael Guennebaud <gael.guennebaud@xxxxxxxxx> wrote:

On Thu, Feb 28, 2013 at 10:00 PM, Sameer AgarwalSIMD is disabled only for fixed size matrices which are supposed to be

<sameeragarwal@xxxxxxxxxx> wrote:

> So why is SIMD/vectorization disabled on unaligned matrices? and by turn on

> Map objects.

small, and so not worth trying to adapt to the actual alignement at

run-time. For dynamic size matrices, everything is vectorized.

Two questions here.

1. So I can take an arbitrary block of memory, make a Map object out of it and all operations on it are vectorized?

2. What are the performance implications of using an Aligned versus unaligned matrix then? Because in the automatic differentiation code in Ceres, where we use unaligned eigen matrices, declaring them aligned makes a 2x difference in performance. And we thought this was because vectorization was getting disabled.

Sameer

**Follow-Ups**:**Re: [eigen] Matrix decompositions***From:*Gael Guennebaud

**References**:**[eigen] Matrix decompositions***From:*Sameer Agarwal

**Re: [eigen] Matrix decompositions***From:*Gael Guennebaud

**Re: [eigen] Matrix decompositions***From:*Sameer Agarwal

**Re: [eigen] Matrix decompositions***From:*Gael Guennebaud

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