Comment by kevmo314

We have this issue in GFQL right now. We wrote the first OSS GPU cypher query language impl, where we make a query plan of gpu-friendly collective operations... But today their steps are coordinated via the python, which has high constant overheads.

We are looking to shed something of the python<->c++<->GPU overheads by pushing macro steps out of python and into C++. However, it'd probably be way better to skip all the CPU<>GPU back-and-forth by coordinating the task queue in the GPU to beginwith . It's 2026 so ideally we can use modern tools and type as safety for this.

Note: I looked at the company's GitHub and didn't see any relevant oss, which changes the calculus for a team like our's. Sustainable infra is hard!

> It's not like CPUs are slow, in fact they're quite a bit faster than any single GPU thread.

This was overwhelmingly true ten years ago, not so much now.

Modern GPU threads are about 3Ghz, CPUs are still slightly faster in theory but the larger amounts of local fast memory makes GPU threads pretty competitive in practice.

I've seen this objection pop up every single time and I still don't get it.

GPUs run 32, 64 or even 128 vector lanes at once. If you have a block of Rust threads that are properly programmed to take advantage of the vector processing by avoiding divergence, etc how is it supposed to be slower?

Consider the following:

You have a hyperoptimized matrix multiplication kernel and you also have your inference engine code that previously ran on the CPU. You now port the critical inference engine code to directly run on the GPU, thereby implementing paged attention, prefix caching, avoiding data transfers, context switches, etc. You still call into your optimized GPU kernels.

Where is the magical slowdown supposed to come from? The mega kernel researchers are moving more and more code to the GPU and they got more performance out of it.

Is it really that hard to understand that the CUDA style programming model is inherently inflexible and limiting? I think the fundamental problem here is that Nvidia marketing gave an incredibly misleading perception of how the hardware actually works. GPUs don't have thousands of cores like CUDA Core marketing suggests. They have a hundred "barrel CPU"-like cores.

The RTX 5090 is advertised to have 21760 CUDA cores. This is a meaningless number in practice since the "CUDA cores" are purely a software concept that doesn't exist in hardware. The vector processing units are not cores. The RTX 5090 actually has 170 streaming multiprocessors each with their own instruction pointer that you can target independently just like a CPU. The key restriction here is that if you want maximum performance you need to take advantage of all 128 lanes and you also need enough thread copies that only differ in the subset of data they process so that the GPU can switch between them while it is working on multi cycle instructions (memory loads and the like). That's it.

Here is what you can do: You can take a bunch of streaming processors, lets say 8 and use them to run your management code on the GPU side without having to transfer data back to the CPU. When you want to do heavy lifting you are in luck, because you still have 162 streaming processors left to do whatever you want. You proceed to call into cuDNN and get great performance.

Additionally there is still too much performance left on the table by not properly using CPU vector units.