This is an abstract curiosity. Let’s say I want to use an old laptop to run a LLM AI. I assume I would still need pytorch, transformers, etc. What is the absolute minimum system configuration required to avoid overhead such as schedulers, kernel threads, virtual memory, etc. Are there options to expose the bare metal and use a networked machine to manage overhead? Maybe a way to connect the extra machine as if it is an extra CPU socket or NUMA module? Basically, I want to turn an entire system into a dedicated AI compute module.
Thanks. I went down this rabbit hole already with large FreeCAD assemblies where single threaded operations had an enormous amount of non voluntary context switching. It helped some in that instance. I don’t have a benchmark, but I reduced the NVCS by an order of magnitude using Tuna to set affinity and pinning. Editing a large assembly in the middle of the tree went from 5++ minutes to around 3-4 minutes.
I’m not claiming I know better, or that this is a bright idea. As mentioned, it’s an abstract curiosity. Playing with FreeCAD got me interested in the OS on a deeper level, and got me reading as much as I can about the CFS and others. The LLM toolchain I’m working on is to help me follow the CS curriculum posted freely online from UC Berkeley. Ultimately I plan to use a langchain database to help me follow along with the books and transcribed lectures. It is just a hobby interest.
Thanks for the insights!
Actually, simple pinning might be enough if you’re seeing a lot of thrash, but most ml systems have something like openmp to handle that automatically.
That’s interesting, I spent a decade doing hpc and other optimizations for large software on 2 socket systems, there are degenerate cases, which can be fixed, I just doubt they’re here.
Freecad sounds like it was poorly written with a lot of hopping about ram with poor cache localization, which happens but is pretty ugly.
Ml tends to be better behaved, it’s actually very close to dsp code and the compilers try to enforce locality, more importantly a lot of the modules are hand coded for extreme performance.
I’m not trying to be that discouraging, I’m saying this as someone who originally looked for performance, and often found it in the os, but later found more performance in the loops themselves or the compiler, basically linux is a lot smarter than it used to be, and many applications are too.
Just my 2c, there are performance tools that can tell you how bad the os is vs other things, and you shouldn’t be swapping so much that it hurts you a lot in ml.