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I have correcte some typos, please read again...

Hello,



If you have noticed i have just come up with a new algorithm of my 
Parallel conjugate gradient solver library that is NUMA aware, this is 
really a big improvement over my previous algorithm,my new algorithm 
contains two parts that are the most expensive, and those two parts are: 
a vector multiplication by a transpose of a matrix, and a vector 
multiplication by a matrix, but when i have parallelized my previous 
algorithm, i have parallelized just the memory data cache transfer from 
the L2 cache-line hit to the CPU that costs around 10 CPU cycles for 
every double type, and i have parallelized also the multiplication of 
two doubles and addition of two doubles, but this was not enough, cause 
what we have to do also is parallelize the memory data transfers from 
the memory to the L2 cache , and this is what we call a NUMA aware 
algorithm that really scale on NUMA architecture, and this is what i 
have done in my new algorithm, the memory data transfers from memory to 
the L2 cache was also parallelized and this have made my new algorithm 
NUMA aware and really scalable on NUMA architecture. But to become NUMA 
aware you need also  to allocate memory of the arrays of your Matrix in 
different NUMA nodes, but that's easy to do.

My Parallel conjugate gradient solver library supports dense matrices 
and it is a library that solves linear system of equations and also it 
solves large and very large dense linear system of equations.


You can download Parallel conjugate gradient solver library from:


https://sites.google.com/site/aminer68/parallel-implementation-of-conjugate-gradient-linear-system-solver


Thank you,
Amine Moulay Ramdane.

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Please read again Ramine <ramine@1.1> - 2014-12-11 16:50 -0800

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