Ramine
4/15/2016 11:23:00 PM
Hello,
I have come to an interesting subject..
Hope you have read my previous critics about USL..
I will ask now the following question:
Is USL methodology still useful ?
Here is my answer:
Since the USL methodology is using polynomial or
nonlinear regression on the empirical statistical
performance data, so my proof and more intuitive
affirmation [1] that i stated before says:
"I have just thought about his methodology of using Parallel
systems as a black box using mathematical regression to quantify
and predict scalability, and i think he is wrong doing it so,
because when the serial part is for example 1/8 of the parallel part,
you can probabilistically get much more scalability with less parallel
threads and less cores than when testing with more and more cores,
because with more and more cores you will get more and more contention
on the serial part , so the Dr. Gunther methodology fails , because
it is testing the system with less cores and less threads, so it will
not get and predict the right or a better approximation of the
scalability" [1]
But this is not the end of the story, in fact this kind
of statistical regression can for example give an optimistic forecast on
approximatly 2X or 3X of the maximum number of cores of the empirical
test that we have obtained with raytracer performance data, because
since it is a regression so it can foerecast optimistically this far.
So this is why my USL programs are still useful, so
i will add to my USL programs nonlinear regression,
so stay tuned because version 3.0 of my USL programs are
coming soon...
Thank you,
Amine Moulay Ramdane.