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<div class="moz-cite-prefix">Hi Lennart,<br>
<br>
This seems alright to me. The most time consuming step is
multiplication of the variance-covariance matrix, which I
optimised. Since this is the same, I do not expect any big change
in performance. With every commit done for ProbABEL my personal
Jenkins instance benchmark 3 scenario's:<br>
<br>
Palinear with DatABEL files
((Npeople=3485,Npredictor=1,Nsnp=33815,correct for sex and age)<br>
Palinear with mldose files
(Npeople=3485,Npredictor=1,Nsnp=33815,correct for sex and age)<br>
Palinear with DatABEL files and mmscore(
Npeople=500,Npredictor=1,Nsnp=1000).<br>
<br>
When there is a slow down (or speed up!) I will inform you.<br>
<br>
Kind regards,<br>
<br>
Maarten<br>
<br>
<br>
<pre wrap="">
</pre>
On 09-05-14 17:03, L.C. Karssen wrote:<br>
</div>
<blockquote cite="mid:536CEE5C.3000207@karssen.org" type="cite">
<pre wrap="">Dear list (and Maarten in particular),
I've written some Doxygen documentation for the mmscore_regression()
function that Maarten recently created. While doing so I changed some of
the variable names to be a bit more understandable and documented what
the function does according to Diego's suggestion on this list some time
ago. I also slightly changed the variables that are created in the
function to get rid of one transpose() action.
My question is: could you have a look at the code (lines 57--62) in the
attached file and see if this still preservers the vectorisation
potential as mentioned in Maarten's comment in the code?
The function can be found in reg1.cpp.
Thanks a lot,
Lennart.
</pre>
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