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Scalable Parallel Conjugate Gradient Linear System solver library
Ramine
2/7/2015 11:49:00 PM
Hello..
My Scalable Parallel implementation of Conjugate Gradient Linear System
solver library that is NUMA-aware and cache-aware is here...
I have spook before about my Parallel Conjugate gradient linear system
solver library and i have said that it is using a probabilistic
mechanism that is very efficient and that render my parallel algorithm
into a scalable parallel algorithm on NUMA architecture, so now i will
prove to you what i am saying:
If you look at my parallel algorithm it is dividing the each array by
250 elements, and if you look carefully i am using two functions that
consumes the greater part of all the CPU, it is the atsub() and asub(),
and inside those functions i am using a probabilistic mechanism so that
to render my algorithm scalable on NUMA architecture, what i am doing is
scrambling the array parts using a probabilistic function
and what i have noticed that this probabilitic mechanism is very
efficient, to proove to you what i am saying , please look at the
following simulation that i have done using a variable that
contains the number of NUMA nodes, and what i have noticed that my
simulation is giving almost a perfect scalability on NUMA architecture,
for example let us give to the "NUMA_nodes" variable a value of 4, and
to our array a value of 250, the simulation bellow will give a number of
contention points of a quarter of the array, so if i am using 16 cores ,
in the the worst case it will scale 4X throughput on NUMA architecture,
because since i am using an array of 250 and there is a quarter of the
array of contention points , so from the Amdahl's law this will give a
scalability of almost 4X throughput on four NUMA nodes, and this will
give almost a perfect scalability on more and more NUMA nodes, so my
parallel algorithm is scalable on NUMA architecture,
Here is the simulation that i have done, please run it and you will notice
yourself that my parallel algorithm is scalable on NUMA architecture.
Here it is:
---
program test;
uses math;
var tab,tab1,tab2,tab3:array of integer;
a,n1,k,i,n2,tmp,j,numa_nodes:integer;
begin
a:=250;
Numa_nodes:=4;
setlength(tab2,a);
for i:=0 to a-1
do
begin
tab2[i]:=i mod numa_nodes;
end;
setlength(tab,a);
randomize;
for k:=0 to a-1
do tab[k]:=k;
n2:=a-1;
for k:=0 to a-1
do
begin
n1:=random(n2);
tmp:=tab[k];
tab[k]:=tab[n1];
tab[n1]:=tmp;
end;
setlength(tab1,a);
randomize;
for k:=0 to a-1
do tab1[k]:=k;
n2:=a-1;
for k:=0 to a-1
do
begin
n1:=random(n2);
tmp:=tab1[k];
tab1[k]:=tab1[n1];
tab1[n1]:=tmp;
end;
for i:=0 to a-1
do
if tab2[tab[i]]=tab2[tab1[i]] then
begin
inc(j);
writeln('A contention at: ',i);
end;
writeln('Number of contention points: ',j);
setlength(tab,0);
setlength(tab1,0);
setlength(tab2,0);
end.
---
You can download my Parallel Conjugate gradient solver library from:
https://sites.google.com/site/aminer68/scalable-parallel-implementation-of-conjugate-gradient-linear-system-solver-library-that-is-numa-aware-and-c...
Thank you for your time.
Thank you for your time.
Amine Moulay Ramdane.
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