More precision about my Scalable Parallel C++ Conjugate Gradient Linear System Solver Library

[Sky89]

Hello..

More precision about my Scalable Parallel C++ Conjugate Gradient Linear
System Solver Library:

Sparse linear system solvers are ubiquitous in high performance
computing (HPC) and often are the most computational intensive parts in
scientific computing codes. A few of the many applications relying on
sparse linear solvers include fusion energy simulation, space weather
simulation, climate modeling, and environmental modeling, and
finite element method, and large-scale reservoir simulations to enhance
oil recovery by the oil and gas industry. .

My Scalable Parallel C++ Conjugate Gradient Linear System Solver
Library for Windows and Linux was updated to version 1.70,
it was more optimized, and this version is much more stable
and scalable and very fast:

Author: Amine Moulay Ramdane

Description:

This 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.

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.


You can download it from:

https://sites.google.com/site/aminer68/scalable-parallel-c-conjugate-gradient-linear-system-solver-library

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)




Thank you,
Amine Moulay Ramdane.