Tuesday, April 24, 2018

Digest for comp.programming.threads@googlegroups.com - 3 updates in 3 topics

Sky89 <Sky89@sky68.com>: Apr 23 08:16PM -0400

Hello,
 
Read this:
 
 
More precision about my new scalable algorithm..
 
As you have noticed i have implemented a new scalable algorithm
for Scalable Parallel C++ and Delphi Conjugate Gradient Linear System
Solver Library, why i have choosen Conjugate Gradient ? here is also
why:
 
For instance, Conjugate Gradient is known to converge to the exact
solution in n steps for a matrix of size n, and was historically first
seen as a direct method because of this. However, after a while people
figured out that it works really well if you just stop the iteration
much earlier - often you will get a very good approximation after much
fewer than n steps. In fact, we can analyze how fast Conjugate gradient
converges. The end result is that Conjugate gradient is used as an
iterative method for large linear systems today.
 
Hope you will be happy with my new scalable algorithm of my following
Scalable Parallel C++ Conjugate Gradient Linear System Solver Library,
my Scalable Parallel C++ Conjugate Gradient Linear System Solver Library
is especially designed for large scale industrial engineering problems
that you find on industrial Finite element problems and such, this
scalable Parallel library was ported to FreePascal and all the Delphi
versions and even to Delphi 7 and also to C++, hope you will find it
really good, and it works on Linux and Windows.
 
The Windows version of my Library for both Delphi and C++ supports
processors groups so that it can use more than 64 logical processors ,
and it is NUMA-aware and cache-aware, please look at the source code
to notice it.
 
I have just read and understood the following paper about
Preconditioning Techniques Analysis for CG Method:
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.592.1575&rep=rep1&type=pdf
 
 
As you have noticed that i have implemented my following Scalable
Parallel C++ Conjugate Gradient Linear System Solver Library ,
it doesn't implement preconditionning techniques because it is
fully "scalable" on multicore and NUMA systems, my library contains a
Scalable Parallel implementation of Conjugate Gradient Dense Linear
System Solver library that is NUMA-aware and cache-aware, and it
contains also a Scalable Parallel implementation of Conjugate Gradient
Sparse Linear System Solver library that is cache-aware.
 
Please download the zip file and read the readme file inside the
zip to know how to use it.
 
Language: GNU C++ and Visual C++ and C++Builder
 
Operating Systems: Windows, Linux, Unix and Mac OS X on (x86)
 
 
You can download my Scalable Parallel C++ Conjugate Gradient Linear
System Solver Library from:
 
https://sites.google.com/site/aminer68/scalable-parallel-c-conjugate-gradient-linear-system-solver-library
 
 
Thank you,
Amine Moulay Ramdane.
Sky89 <Sky89@sky68.com>: Apr 23 07:50PM -0400

Hello,
 
Read this:
 
Hope you will be happy with my new scalable algorithm of my following
Scalable Parallel C++ Conjugate Gradient Linear System Solver Library,
my Scalable Parallel C++ Conjugate Gradient Linear System Solver Library
is especially designed for large scale industrial engineering problems
that you find on industrial Finite element problems and such, this
scalable Parallel library was ported to FreePascal and all the Delphi
versions and even to Delphi 7 and also to C++, hope you will find it
really good, and it works on Linux and Windows.
 
The Windows version of my Library for both Delphi and C++ supports
processors groups so that it can use more than 64 logical processors ,
and it is NUMA-aware and cache-aware, please look at the source code
to notice it.
 
I have just read and understood the following paper about
Preconditioning Techniques Analysis for CG Method:
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.592.1575&rep=rep1&type=pdf
 
 
As you have noticed that i have implemented my following Scalable
Parallel C++ Conjugate Gradient Linear System Solver Library ,
it doesn't implement preconditionning techniques because it is
fully "scalable" on multicore and NUMA systems, my library contains a
Scalable Parallel implementation of Conjugate Gradient Dense Linear
System Solver library that is NUMA-aware and cache-aware, and it
contains also a Scalable Parallel implementation of Conjugate Gradient
Sparse Linear System Solver library that is cache-aware.
 
Please download the zip file and read the readme file inside the
zip to know how to use it.
 
Language: GNU C++ and Visual C++ and C++Builder
 
Operating Systems: Windows, Linux, Unix and Mac OS X on (x86)
 
 
You can download my Scalable Parallel C++ Conjugate Gradient Linear
System Solver Library from:
 
https://sites.google.com/site/aminer68/scalable-parallel-c-conjugate-gradient-linear-system-solver-library
 
 
Thank you,
Amine Moulay Ramdane.
Sky89 <Sky89@sky68.com>: Apr 23 06:23PM -0400

Hello..
 
Read this:
 
 
I have just read and understood the following paper about
Preconditioning Techniques Analysis for CG Method:
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.592.1575&rep=rep1&type=pdf
 
 
As you have noticed that i have implemented my following Scalable
Parallel C++ Conjugate Gradient Linear System Solver Library ,
it doesn't implement preconditionning techniques because it is
fully "scalable" on multicore and NUMA systems, my library contains a
Scalable Parallel implementation of Conjugate Gradient Dense Linear
System Solver library that is NUMA-aware and cache-aware, and it
contains also a Scalable Parallel implementation of Conjugate Gradient
Sparse Linear System Solver library that is cache-aware.
 
Please download the zip file and read the readme file inside the
zip to know how to use it.
 
Language: GNU C++ and Visual C++ and C++Builder
 
Operating Systems: Windows, Linux, Unix and Mac OS X on (x86)
 
 
You can download my Scalable Parallel C++ Conjugate Gradient Linear
System Solver Library from:
 
https://sites.google.com/site/aminer68/scalable-parallel-c-conjugate-gradient-linear-system-solver-library
 
 
Thank you,
Amine Moulay Ramdane.
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