A fun experiment but I wonder how many out there seriously think we could ever completely rid ourselves of the CPU. It seems to be a rising sentiment.
The cost of communicating information through space is dealt with in fundamentally different ways here. On the CPU it is addressed directly. The actual latency is minimized as much as possible, usually by predicting the future in various ways and keeping the spatial extent of each device (core complex) as small as possible. The GPU hides latency with massive parallelism. That's why we can put them across relatively slow networks and still see excellent performance.
Latency hiding cannot deal well in workloads that are branchy and serialized because you can only have one logical thread throughout. The CPU dominates this area because it doesn't cheat. It directly targets the objective. Making efficient, accurate control flow decisions tends to be more valuable than being able to process data in large volumes. It just happens that there are a few exceptions to this rule that are incredibly popular.
> I wonder how many out there seriously think we could ever completely rid ourselves of the CPU.
How do you class systems like the PS5 that have an APU plugged into GDDR instead of regular RAM? The primary remaining issue is the limited memory capacity.
I wonder if we might see a system with GPU class HBM on the package in lieu of VRAM coupled with regular RAM on the board for the CPU portion?
To multiply two arbitrary numbers in a single cycle, you need to include dedicated hardware into your ALU, without it you have to combine several additions and logical shifts.
As to why not use the ADD/MUL capabilities of the GPU itself, I guess it wasn’t in the spirit of the challenge. ;)
This is a fun idea. What surprised me is the inversion where MUL ends up faster than ADD because the neural LUT removes sequential dependency while the adder still needs prefix stages.
This CPU simulator does not attempt to achieve the maximum speed that could be obtained when simulating a CPU on a GPU.
For that a completely different approach would be needed, e.g. by implementing something akin to qemu, where each CPU instruction would be translated into a graphic shader program. On many older GPUs, it is impossible or difficult to launch a graphic program from inside a graphic program (instead of from the CPU), but where this is possible one could obtain a CPU emulation that would be many orders of magnitude faster than what is demonstrated here.
Instead of going for speed, the project demonstrates a simpler self-contained implementation based on the same kind of neural networks used for ML/AI, which might work even on an NPU, not only on a GPU.
Because it uses inappropriate hardware execution units, the speed is modest and the speed ratios between different kinds of instructions are weird, but nonetheless this is an impressive achievement, i.e. simulating the complete Aarch64 ISA with such means.
Every clueless person who suggest that we move to GPUs entirely have zero idea how things work and basically are suggesting using lambos to plow fields and tractors to race in nascar
That would be cool. A way to read cpu assembly bytecode and then think in it.
It's slower than real cpu code obviously but fast just 'thinking' about it. They wouldn't need to actually simulate an entire program in a never ending hot loop like a real program.
You're both completely missing the point. It's important that an LLM be able to perform exact arithmetic reliably without a tool call. Of course the underlying hardware does so extremely rapidly, that's not the point.
Ya know just today I was thinking around a way to compile a neural network down to assembly. Matching and replacing neural network structures with their closest machine code equivalent.
This is way cooler though! Instead of efficiently running a neural network on a CPU, I can inefficiently run my CPU on neural network! With the work being done to make more powerful GPUs and ASICs I bet in a few years I'll be able to run a 486 at 100MHz(!!) with power consumption just under a megawatt! The mind boggles at the sort of computations this will unlock!
Few more years and I'll even be able to realise the dream of self-hosting ChatGPT on my own neural network simulated CPU!
I imagine a carefully crafted set of programming primitives used to build up the abstraction of a CPU…
“Every ALU operation is a trained neural network.”
Oh… oh. Fun. Just not the type of “interesting” I was hoping for.
[1]: https://breandan.net/2020/06/30/graph-computation#roadmap
The cost of communicating information through space is dealt with in fundamentally different ways here. On the CPU it is addressed directly. The actual latency is minimized as much as possible, usually by predicting the future in various ways and keeping the spatial extent of each device (core complex) as small as possible. The GPU hides latency with massive parallelism. That's why we can put them across relatively slow networks and still see excellent performance.
Latency hiding cannot deal well in workloads that are branchy and serialized because you can only have one logical thread throughout. The CPU dominates this area because it doesn't cheat. It directly targets the objective. Making efficient, accurate control flow decisions tends to be more valuable than being able to process data in large volumes. It just happens that there are a few exceptions to this rule that are incredibly popular.
How do you class systems like the PS5 that have an APU plugged into GDDR instead of regular RAM? The primary remaining issue is the limited memory capacity.
I wonder if we might see a system with GPU class HBM on the package in lieu of VRAM coupled with regular RAM on the board for the CPU portion?
I can see the same happening to the CPU. It will just take on the appropriate functionality to keep all the compute in the same chip.
It’s gonna take awhile because Nvidia et al like their moats.
Also, is it possible to use the GPU's ADD/MUL implementation? It is what a GPU does best.
As to why not use the ADD/MUL capabilities of the GPU itself, I guess it wasn’t in the spirit of the challenge. ;)
Most GPUs, sitting in racks in datacenters, aren't "processing graphics" anyhow.
Gross-Parallelization Units
Generative Procedure Units
Gratuitously Profiteering Unscrupulously
[0]: https://en.wikipedia.org/wiki/General-purpose_computing_on_g...
Wow. That's cool but what happens to the regular CPU?
For that a completely different approach would be needed, e.g. by implementing something akin to qemu, where each CPU instruction would be translated into a graphic shader program. On many older GPUs, it is impossible or difficult to launch a graphic program from inside a graphic program (instead of from the CPU), but where this is possible one could obtain a CPU emulation that would be many orders of magnitude faster than what is demonstrated here.
Instead of going for speed, the project demonstrates a simpler self-contained implementation based on the same kind of neural networks used for ML/AI, which might work even on an NPU, not only on a GPU.
Because it uses inappropriate hardware execution units, the speed is modest and the speed ratios between different kinds of instructions are weird, but nonetheless this is an impressive achievement, i.e. simulating the complete Aarch64 ISA with such means.
You really think having a shader per CPU-instruction is going to get you closer to the highest possible speed one can achieve?
It's slower than real cpu code obviously but fast just 'thinking' about it. They wouldn't need to actually simulate an entire program in a never ending hot loop like a real program.
This is all a computer does :P
We need llms to be able to tap that not add the same functionality a layer above and MUCH less efficiently.
Agents, tool-integrated reasoning, even chain of thought (limited, for some math) can address this.
This is way cooler though! Instead of efficiently running a neural network on a CPU, I can inefficiently run my CPU on neural network! With the work being done to make more powerful GPUs and ASICs I bet in a few years I'll be able to run a 486 at 100MHz(!!) with power consumption just under a megawatt! The mind boggles at the sort of computations this will unlock!
Few more years and I'll even be able to realise the dream of self-hosting ChatGPT on my own neural network simulated CPU!