Re: Speed Test For Big-Mouthed Lackeys

On Tue, 08 Apr 2025 21:03:07 +0000, Farley Flud <ff@linux.rocks> wrote in
<pan$35924$463a0a60$2d7b1a01$1cb6757b@linux.rocks>:

There are a lot of fat-mouthed distro lackeys on this group that claim
that a generic distro is just as fast as any optimized system.
 
Well, it's time for them to put their money where their fat mouths are.
 
Download and build "msieve" which is a program to factor very large
integers:
 
https://sourceforge.net/projects/msieve/files/msieve/
 
Perhaps your distro already has msieve ready to rock and roll.
Gentoo certainly does.  So too does ArchLinux.
 
Whatever, be sure to use msieve that is NOT built to use the GPU (via
CODA) but only the CPU.
 
Then factor the following 159-digit integer:
 
 

343065624301876906744214935814545423476905577648850799588019249360798320538732597045829167171100863421689224335886880379963254179442200201724690242850228936603

 
Report the time required.  Msieve will output the time upon completion.

I notice you didn't report the time it takes your machine to factor this
number.

My customized 12-year-old Core i7 will doubtless beat the pants over all
recent systems using a genric distro.

 From the Readme:

 _ _ _ _ _ _ _
The maximum size of numbers that can be given to the library is hardwired
at compile time. Currently the code can handle numbers up to ~310 digits;
however, you should bear in mind that I don't expect the library to be
able to complete a factorization larger than about 120 digits by itself.
The larger size inputs can only really be handled by the number field
sieve, and the NFS sieving code is not efficient or robust enough to deal
with problems larger than that. Msieve *can* complete very large NFS
factorizations as long as you use the NFS sieving tools from other open-
source packages.
 _ _ _ _ _ _ _

The fact that this runs on a single core, and has logical errors in the
code, should be noted.

Finally, the artificial limit that one can't use CUDA doesn't jive well
with those of us who have more advanced computational workstations than
you do.

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