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.
volemo 22 minutes ago [-]
I see us not getting rid of CPU, but CPU and GPU being eventually consolidated in one system of heterogeneous computing units.
I was taught years ago that MUL and ADD can be implemented in one or a few cycles. They can be the same complexity. What am I missing here?
Also, is it possible to use the GPU's ADD/MUL implementation? It is what a GPU does best.
volemo 10 minutes ago [-]
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. ;)
deep1283 3 hours ago [-]
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.
Wow. That's cool but what happens to the regular CPU?
adrian_b 3 hours ago [-]
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.
5o1ecist 2 hours ago [-]
> where each CPU instruction would be translated into a graphic shader program
You really think having a shader per CPU-instruction is going to get you closer to the highest possible speed one can achieve?
nicman23 4 hours ago [-]
can i run linux on a nvidia card though?
micw 3 hours ago [-]
Linux runs everywhere
volemo 6 minutes ago [-]
Except on my stupid iPad “Pro”. :(
3 hours ago [-]
4 hours ago [-]
mrlonglong 3 hours ago [-]
Now I've seen it all. Time to die.. (meant humourously)
Surac 4 hours ago [-]
Well GPU are just special purpous CPU.
RagnarD 4 hours ago [-]
Being able to perform precise math in an LLM is important, glad to see this.
jdjdndnzn 4 hours ago [-]
Just want to point out this comment is highly ironic.
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.
Nuzzerino 4 hours ago [-]
> 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.
RagnarD 2 hours ago [-]
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.
jdjdndnzn 5 minutes ago [-]
The computer ALREADY does do math reliably. You are missing the point.
5o1ecist 2 hours ago [-]
Why?
MadnessASAP 3 hours ago [-]
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!
Rendered at 10:04:49 GMT+0000 (Coordinated Universal Time) with Vercel.
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.
[1]: https://breandan.net/2020/06/30/graph-computation#roadmap
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?
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!