You reading my replies ?

Hello,

More of my philosophy about the 12 memory channels of
the new AMD Epyc Genoa CPU and more of my thoughts..

I am a white arab, and i think i am smart since i have also
invented many scalable algorithms and algorithms..

So as i am saying below, i think that so that to use 12 memory
channels in parallel that supports it the new AMD Genoa CPU, the GMI-Wide mode must enlarge more and connects each CCD with more GMI links, so i think that it is what is doing AMD in its new 4 CCDs configuration, even with
the costs optimized Epyc Genoa 9124 16 cores with 64 MB of L3 cache with 4 Core Complex Dies (CCDs), that costs around $1000 (Look at it here: https://www.tomshardware.com/reviews/amd-4th-gen-epyc-genoa-9654-9554-and-9374f-review-96-cores-zen-4-and-5nm-disrupt-the-data-center ),
and as i am explaining more below that the Core Complex Dies (CCDs) connect to memory, I/O, and each other through the I/O Die (IOD) and each CCD connects to the IOD via a dedicated high-speed, or Global Memory Interconnect (GMI) link and the IOD also contains memory channels, PCIe Gen5 lanes, and Infinity Fabric links and all dies, or chiplets, interconnect with each other via AMD’s Infinity Fabric Technology, and of course this will permit my new software project of Parallel C++ Conjugate Gradient Linear System Solver Library that scales very well to scale on the 12 memory channels, read my following thoughts so that to understand more about it:

More of my philosophy about the new Zen 4 AMD Ryzen™ 9 7950X and more of my thoughts..

So i have just looked at the new Zen 4 AMD Ryzen™ 9 7950X CPU, and i invite you to look at it here:

https://www.amd.com/en/products/cpu/amd-ryzen-9-7950x

But notice carefully that the problem is with the number of supported memory channels, since it just support two memory channels, so it is not good, since for example my following Open source software project of Parallel C++ Conjugate Gradient Linear System Solver Library that scales very well is scaling around 8X on my 16 cores Intel Xeon with 2 NUMA nodes and with 8 memory channels, but it will not scale correctly on the
new Zen 4 AMD Ryzen™ 9 7950X CPU with just 2 memory channels since it is also memory-bound, and here is my Powerful Open source software project of Parallel C++ Conjugate Gradient Linear System Solver Library that scales very well and i invite you to take carefully a look at it:

https://sites.google.com/site/scalable68/scalable-parallel-c-conjugate-gradient-linear-system-solver-library

So i advice you to buy an AMD Epyc CPU or an Intel Xeon CPU that supports 8 memory channels.

---

And of course you can use the next Twelve DDR5 Memory Channels for Zen 4 AMD EPYC CPUs so that to scale more my above algorithm, and read about it here:

https://www.tomshardware.com/news/amd-confirms-12-ddr5-memory-channels-on-genoa

And here is the simulation program that uses the probabilistic mechanism that i have talked about and that prove to you that my algorithm of my Parallel C++ Conjugate Gradient Linear System Solver Library is scalable:

If you look at my scalable parallel algorithm, it is dividing the each array of the matrix 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 , and it also make it scale on the memory channels, what i am doing is scrambling the array parts using a probabilistic function and what i have noticed that this probabilistic mechanism is very efficient, to prove 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 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 and it also scale well on the memory channels,

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 mod numa_nodes;

end;

setlength(tab,a);

randomize;

for k:=0 to a-1
do tab:=k;

n2:=a-1;

for k:=0 to a-1
do
begin
n1:=random(n2);
tmp:=tab;
tab:=tab[n1];
tab[n1]:=tmp;
end;

setlength(tab1,a);

randomize;

for k:=0 to a-1
do tab1:=k;

n2:=a-1;

for k:=0 to a-1
do
begin
n1:=random(n2);
tmp:=tab1;
tab1:=tab1[n1];
tab1[n1]:=tmp;
end;

for i:=0 to a-1
do
if tab2[tab]=tab2[tab1] 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.
---

More of my philosophy about 4 CCDs configuration of AMD Epyc Genoa CPU and more of my thoughts..

I have just read the following new paper about AMD 4th Gen EPYC 9004 Series, so i invite you to read it carefully:

https://hothardware.com/reviews/amd-genoa-data-center-cpu-launch

So read carefully the 4 CCDs configuration, so i am understanding
the following from it:

I/O DIE is what is connected to the memory channels externally, and it says that SKUs north of 4 CCDs (e.g. 32 cores) use the GMI3-Narrow configuration with a single GMI link per CCD. With 4 CCD and lower SKUs, AMD can implement GMI-Wide mode which joins each CCD to the IOD with two GMI links. In this case, one link of each CCD populates GMI0 to GMI3 while the other link of each CCD populates GMI8 to GMI11 as diagramed above. This helps these parts better balance against I/O demands.

So i think that that AMD implemented in his new 4 CCDs configuration the GMI-Wide mode which joins each CCD to the IOD with two GMI links, so that to be connected to the 8 memory channels externally and use them in parallel, so i think that the problem is solved, since i think that the cost optimized Epyc Genoa 9124 16 cores with 64 MB of L3 cache with 4 Core Complex Dies (CCDs), that costs around $1000 (Look at it here: https://www.tomshardware..com/reviews/amd-4th-gen-epyc-genoa-9654-9554-and-9374f-review-96-cores-zen-4-and-5nm-disrupt-the-data-center )
can use fully the 8 memory channels in parallel, so it is a good Epyc Genoa processor to buy.

And of course i invite you to read the following:

More of my philosophy about the new Epyc Genoa and about Core Complex Die (CCD) and Core-complex(CCX) and more of my thoughts..

I have just looked at the following paper from AMD and i invite
you to look at it:

https://developer.amd.com/wp-content/resources/56827-1-0.pdf

And as you notice above that you have to look at how many
Core Complex Dies (CCDs) you have, since it tells you more
about how many connections of Infinity Fabric you have, and it is
an important information, since look at the following article
about the new AMD Epyc Genoa:

https://wccftech.com/amd-epyc-genoa-cpu-lineup-specs-benchmarks-leak-up-to-2-6x-faster-than-intel-xeon/

More of my thoughts about technology and about Apple Silicon M1 Emulating x86 and more of my thoughts..

I have just looked at the following articles about Rosetta 2 and the benchmarks of Apple Silicon M1 Emulating x86:

https://www.computerworld.com/article/3597949/everything-you-need-to-know-about-rosetta-2-on-apple-silicon-macs.html

and read also here:

https://www.macrumors.com/2020/11/15/m1-chip-emulating-x86-benchmark/

But i think that the problem with Apple Silicon M1 and the next Apple Silicon M2 is that Rosetta 2 only lets you run x86–64 macOS apps. That would be apps that were built for macOS (not Windows) and aren't 32-bit. The macOS restriction eliminates huge numbers of Windows apps, and 64-bit restriction eliminates even more.

Also read the following:

Apple says new M2 chip won’t beat Intel’s finest

Read more here:

https://www.pcworld.com/article/782139/apple-m2-chip-wont-beat-intels-finest.html

And here is what i am saying on my following thoughts about technology about Arm Vs. X86:

More of my philosophy about the Apple Silicon and about Arm Vs. X86 and more of my thoughts..

I invite you to read carefully the following interesting article so
that to understand more:

Overhyped Apple Silicon: Arm Vs. X86 Is Irrelevant

https://seekingalpha.com/article/4447703-overhyped-apple-silicon-arm-vs-x86-is-irrelevant

More of my philosophy about code compression of RISC-V and ARM and more of my thoughts..

I think i am highly smart, and i have just read the following paper
that says that RISC-V Compressed programs are 25% smaller than RISC-V programs, fetch 25% fewer instruction bits than RISC-V programs, and incur fewer instruction cache misses. Its code size is competitive with other compressed RISCs. RVC is expected to improve the performance and energy per operation of RISC-V.