Oyasumibutts

have you tried the new gefs
aaa i want a raspberry to run 9front

>>>/watch?v=bmbAm-fKnbQ

>>>/watch?v=yPQ8rSINMNQ

>>21725
I have it on my THINKPAD wwww
seems ok, prefer cwfs for the big fucking server though
>>21726
whoa, GUMI
where's that fucking nerd GUMI cosplayer

gumi cutie

Horse girl kiggers to the kigger glue factory

same

>>21732
wow
homu

Who homu

program doesn't work at all but technically not a supported OS release
thank you theo

kill everyone who has ever written an ifdef

plop tuah

Kill every horse

horse genocide now

Oyasumi bunbune

noooo vip

Awaken burgfigerd

Borgfrogor

borgar

Bordigar

Trajodie of the plopping

BUN

>>21757
the size of BUN

Tsogre

#plop (42)

Flens

Nite busts

Ploppy morning

I wish the linux network stack developers a very DIE
vmbr isolation on boot wow

i know the first three numbers

>>21771
im in
your area

Synced
Not Cenk'd

nippah

What keeps plopkind alive

Agua…

nitebuns

videogames

bideogaimes

>>21788
plydypglymes

more like
video GAYMES

now ai
deogames

plopyogames

Destroy horse

hey vip I got a programming question for you
I'm struggling to make a parallel code base run well. I'm not sure how to debug things, so, here we go:
1. I have a n algorithm with 20 MB of RAM required to run on ~2 MB of data.
2. I have 112 CPU cores.
The sets of 2 MB of data are.. basically infinite. Like, I'll have 2000 of these chunks of data and I need to run the algorithm on it 2000 times.
The issue I get is:
Parallelizing the algorithm where I copy the 20 MB onto each core and running on the 2 MB of data requires a massive overhead and it's incredibly slow.
If I put the algorithm in shared memory, this cleans up the overhead a bit, but I run into issues where the chunks of data aren't perfectly equal, so some finish earlier than others
And then I have a join() call where one core is just chugging along. This is exacerbated when I split up the algorithms into its own set of cores - for instance, I copy the algorithm 1 core and run 4 sets of data, do this over 112 cores.

Basically -- do you have other ideas on what I can try when working with a hefty algorithm + constant stream of data? I have plenty of CPUs, I just don't know how to utilize them properly.

It's a class that contains a bank of filters
The 20 MB filters are applied to the data and then try to classify the data
So I was copying this class across all my different processes, but when the processes were attached across all the CPUs, I guess the context switching and copying over the data onto each CPU was too much and it went from a 2 minute execution to a 6 hour execution.
When I limit to only 2 CPUs via taskset -c $1 and run it a bunch of times it goes super fast.

Would it be that, because I have, say, 28 processes, it's shoving 20*28 MB into cache?
Sorry, what is MPI?
Well, most likely, no. Ah, no.
I mean I don't wanna say what it is cuz you'll immediately just say "well that's your problem" and end it there lol
But it's Python and honestly I've just been doing "run code & run code1 & run code2 & run code3 &" in bash so it has different processes.
Er:
taskset -c 1,2,3,4 python main.py & taskset -c 5,6,7,8 python main1.py & taskset -c 9,10,11,12 python main2.py & -- etc
When I look at the system-monitor, it seems to properly use those CPUs I've set affinity for
Oh! Speaking of - the thing I'm trying to do is basically set CPU affinity in my program itself. So when I spawn a process and immediately set the CPU affinity to just 1 CPU, but it started at, say, 112 CPUs.. does it immediately copy over every single instance of my class to the 112 CPUs?
I was worried I'm copying data over when I shouldn't be.
I do have it --- okay.. uh, would you be to determine if -- ah hold on

Is there a way for me to track if I am loading 20 MB into each core or if it's simply pulling from shared memory properly?
I'm simply struggling at what I need to look up on the internet for these things.
www ah alright alright. I do have about half of my code written in C and then I call the compiled C via Python, maybe I can just slap a memory util in there as well.
Mmmm you're smart thanks I gotta do that

In Python <--> C, Python has a ctypes library where you point to a shared library and need to initialize your data types on input and output, then you can call the shared library function in Python.

…

Well that was retarded, uh, be careful and if I see your posts get got I’ll unban you www

??
Did you get yourself auto-banned? www

>>21802
Yeah comparing "C" to
Python in that order uh

Oh fuck your message is gone, I refreshed the tab, dammit
>>21803
huh

wwwwww could always bake the filters into the C part's rodata if you want to have FUN with the good old ar(1)
your executable will be fuckhueg but it’ll get copied and do lookups free

I am glad I have something to push forward with now though, cuz I was absolutely stumped.
Thanks!

wow
Zcopy access in python looks like a PITA
amazing, i love programming

oyasumi plopers

Wake the fuck up nugs

maybe plops will be better
in chicago

end of horse

Horse nomore

Beef

Plop tuah

Nite reads

Mrplopbeast

aaaa
aaaaaaaa

im having vivid halucinations or flashbacks or whatever of me having SEX
aaaa

>>21826
waow
wow
SEX
imagine, you could be having SEX
RIGHT NOW
if you just got on public transit wwww

some like it plop

Tellolism

Programming is for nerds

>>21830
ur a nerd

#plop (43)

>>21830
the biggest of nerds
los nerdos

kurds too

nerds, kurds, and turds

Plop tuah