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Let's talk more computer science

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Hello...

Let's talk more computer science...

Finally i have arrived to an important subject that is "optimization",
and you will notice that computer science learns you one
of the most important thing, is how to amortize and optimize by 
optimizing more your time complexity that we express mathematically with 
a O(), this is how i have done it with my new parallel sort algorithm 
that has a "very" good time complexity, and what learns you also 
computer science is how to optimize more in the source code's part or 
parts that takes the greater percentage of running time, and what learns 
you also computer science is to do  the greater percentage of all the 
optimizations and to not  do the smaller optimizations, this is also a 
good way to follow to optimize your code, so if you look at my
parallel conjugate gradient solver library , i am not using SIMD SSE2 
instructions , but i am just parallelizing the memory transfers cause my 
parallel algorithm is NUMA-aware and i am also parallelizing also the 
multiplication and addition of the double's type etc. and also my 
algorithm is cache-aware so i am also parallelizing more the L2 cache 
memory transfers , so this optimization will make it a very greater 
percentage of all the optimization that we can do on my parallel 
algorithm, so in large scale industrial problems i don't think that my 
parallel conjugate gradient algorithm needs to do SIMD SSE2 or AVX 
optimization , cause SIMD SSE2 or AVX will give just a small improvement 
to my parallel algorithm.


You can download my Parallel conjugate gradient linear system solver 
library that is NUMA-aware and cache-aware from:

https://sites.google.com/site/aminer68/scalable-parallel-implementation-of-conjugate-gradient-linear-system-solver-library-that-is-numa-aware-and-cache-aware



Thank you for your time.


Amine Moulay Ramdane.

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Let's talk more computer science Ramine <ramine@1.1> - 2015-01-02 17:53 -0800

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