amx VS mighty-snitch

I am talking about the matrix/vector coprocessor (AMX). You can find some reverse-engineered documentation here: https://github.com/corsix/amx

On M3 a singe matrix block can achieve ~ 1TFLOP on DGEMM, I assume it will be closer to 4TFLOPS for SGEMM. The Max variants have two such blocks. Didn't do precise benchmarking myself, but switching Python/R matrix libraries to use Apple's BLAS result in 5-6x perf improvement on matrix heavy code for me.

It's really cool. I hope it becomes more common for training/inference/numerics capable accelerators to be included in consumer hardware.

Apple's AMX is really under-documented, while the instructions were reverse engineered, Virtually no benchmarks are available comparing current chip generations, models and variants.

https://github.com/corsix/amx

Visible in the unofficial documentation for AMX instructions too - M2 only bf16 functionality - https://github.com/corsix/amx/blob/main/matfp.md

Confusingly there are 2 mechanisms to do matrix operations on the new apple hardware - AMX (https://github.com/corsix/amx) - and the ANE (apple neural engine) - which is enabled by CoreML. This code does not run on the neural engine but the author has a branch for his whisper.cpp project which uses it here: https://github.com/ggerganov/whisper.cpp/pull/566 - so it may not be long before we see it applied here as well. All of this is to say that it actually could get significantly faster if some of this work was able to be handed to the ANE with CoreML.

really? seems pretty well documented here: https://github.com/corsix/amx

Article is almost two years old, and has a huge correction at the bottom. It's just a proprietary ISA extension, there's even a repo documenting what's been reverse engineered.

You are correct, in that those are the four

My understanding is that the AMX is more tightly wound with the CPU, ultimately being accessible via an instruction set (https://github.com/corsix/amx), and it is useful if you need to do matrix multiplications interleaved with other CPU tasks. A common example would be a VIO loop or something where you want that data in the CPU caches.

The GPU and Neural Engine are not that – they take some time to set up and initialize. They also can parallelize tasks to a much higher degree. The GPU is more generalizable, because you can write compute shaders to do anything in parallel, but it uses a lot of resources. I'll have to check out the PR to see how exactly the MPS shaders match up with the task at hand, because you could also consider writing Metal compute shaders by hand.

I know the least about the ANE, but it has specific hardware for running ML models, and you have to process the weights ahead of time to make sure they are in the right format. It can run ML models very efficiently and is the most battery friendly.

If someone were to build a Linux system with proprietary ISA extensions, how would they do it given Linux is open source? Are there any examples of this being done? Would it be possible at all?

I got inspiration from this (https://github.com/corsix/amx) and I wondered if someone has done it before on a Linux-based system. I understand a userspace library could be created to access those instructions from userspace, but how would then they be implemented in the kernel? Through a proprietary kernel module built using a custom compiler? Or is that not needed at all and the library could just run on the processor taking advantage of the proprietary extensions?

6 months in to my first pc build. had one other pc for past 2 years from letsbld.

the build is easy, but it will take a full day. it is also a bit nerve racking, but mostly that’s just inexperience. parts are all sturdy and snap together. i will be building every nvidia generation from now on.

i used to code on laptops, then game on a pc. now i have to ssds, one windows one linux. bios boot option is unborkable. it’s a great setup.

it’s nice to be able to play fortnite, compile the linux kernel[1], and do gamedev[2] all in a single session.

be aware ddr5 amd boards boot kind of slow. i’ve heard intel is faster.

if you can afford it, go god spec. otherwise spec down into your price range. letsbld and originpc have good configurators to choose parts. newegg prices will be 30% less.

check out fractal torrent cases and dh15 coolers! 4090 fe fits in the nano and 4090 pny fits in the compact.

1. https://github.com/nathants/mighty-snitch/tree/master/kernel...

2. https://r2.nathants.workers.dev/jetpack_hand_animations.mp4

learning cloud is not necessarily using cloud for all things.

cloud adds legitimate new capabilities to every engineer.

if i’m on coffeeshop wifi with my low power laptop, and i need to do something intense like compile linux, i’m sol.

unless i know aws. then i can open a new terminal, spin up a massive spot instance for 19.27 minutes, get that done, then self destruct. [1]

being able to test lambda to s3 io, or ec2 to s3 io, with the same ease one uses grep and sed, is for great good. also it’s fun.

1. https://github.com/nathants/mighty-snitch/blob/master/kernel...

you jest, but this is actually fine.

do this via mighty-snitch[1] on any postmarketos phone. any network request passing through the linux kernel gets filtered.

still hosed if it’s hardware level nonsense unfortunately.

the only reason i’m not daily driving postmarketos if lack of gpu acceleration for firefox. hopefully soon!

1. https://github.com/nathants/mighty-snitch

i just built 6.2 for arm64. what timing!

https://github.com/nathants/mighty-snitch/releases

use a network snitch[1] on desktop and mobile. The original slogan says it all: makes the invisible visible. i’d love to use a disk snitch too, but no exist yet afaik.

it’s interesting to see firefox or any other legitimate app i’m using make many unsolicited requests to weird domains. it feels good to interactively deny those connections.

make sure that cloud[2], which includes git hosts[3], are untrusted. unencrypted data should never hit remote. keys should never leave local.

consider the tradeoffs with online interactions. engaging with other humans in public on github and hackernews is likely worth. engaging in impassioned op-ed debate with bots on engagement monetization platforms like twitter or youtube is likely not.

1. https://github.com/nathants/mighty-snitch

2. https://cryptomator.org/

3. https://github.com/nathants/git-remote-aws