11–20 of 209 posts
Having worked for a company that made a "hundreds of small CPUs on a single chip", I can tell you now that they're all going to fail because the programming model is too weird, and nobody will write software for them. Whatever comes next will be a GPU with extra capabilities, not a totally new architecture. Probably an nVidia GPU.
Having worked for a company that made a "hundreds of small CPUs on a single chip", I can tell you now that they're all going to fail because the programming model is too weird, and nobody will write software for them. Whatever comes next will be a GPU with extra capabilities, not a totally new architecture. Probably an nVidia GPU.
Im no expert, but I’ve done my fair share of parallel HPC stuff using MPI, and a little bit of Cuda. And to me the GPU programming model is far far “weirder” and harder to code for than the many-CPUs model. (Granted, I’m assuming you’re describing a different regime?)
It is odd that he talks about Larabee so much, but doesn’t mention the Xeon Phis. (Or is it Xeons Phi?). > As a general trend, CPU designs are diverging into those optimizing single-core performance (performance cores) and those optimizing power efficiency (efficiency cores), with cores of both types commonly present on the same chip. As E-cores become more prevalent, algorithms designed to exploit parallelism at sca…
Having worked for a company that made a "hundreds of small CPUs on a single chip", I can tell you now that they're all going to fail because the programming model is too weird, and nobody will write software for them. Whatever comes next will be a GPU with extra capabilities, not a totally new architecture. Probably an nVidia GPU.
Having worked for a company that made a "hundreds of small CPUs on a single chip", I can tell you now that they're all going to fail because the programming model is too weird, and nobody will write software for them. Whatever comes next will be a GPU with extra capabilities, not a totally new architecture. Probably an nVidia GPU.
Could you elaborate on this? How does many-small-CPUs make for a weirder programming model than a GPU? Im no expert, but I’ve done my fair share of parallel HPC stuff using MPI, and a little bit of Cuda. And to me the GPU programming model is far far “weirder” and harder to code for than the many-CPUs model. (Granted, I’m assuming you’re describing a different regime?)
When programming CPUs however you are controlling and managing the individual threads. Of course, there are libraries which can do that for you, but fundamentally it's a different model.
Having worked for a company that made a "hundreds of small CPUs on a single chip", I can tell you now that they're all going to fail because the programming model is too weird, and nobody will write software for them. Whatever comes next will be a GPU with extra capabilities, not a totally new architecture. Probably an nVidia GPU.
The key transformation required to make any parallel architecture work is going to be taking a program that humans can understand, and translating it into a directed acyclic graph of logical Boolean operations. This type of intermediate representation could then be broken up into little chunks for all those small CPUS. It could be executed very slowly using just a few logic gates and enough ram to hold the state, or…
AMD Strix Halo APU is a CPU with very powerful integrated GPU. It’s faster at AI than an Nvidia RTX4090, because 96GB of the 128GB can be allocated to the GPU memory space. This means it’s doesn’t have the same swapping/memory thrashing that a discrete GPU experiences when processing large models. 16 CPU cores and 40 GPU compute units sounds pretty parallel to me. Doesn’t that fit the bill?
I love AMD's Ryzen chips and will recommend their laptops over an Nvidia model all day. However, this is a pretty facetious comparison that falls apart when you normalize the memory. Any chip can be memory bottlenecked, and if we take away that arbitrary precondition the Strix Halo gets trounced in terms of compute capacity. You can look at the TDP of either chip and surmise this pretty easily.
> The GPU in your computer is about 10 to 100 times more powerful than the CPU, depending on workload. For real-time graphics rendering and machine learning, you are enjoying that power, and doing those workloads on a CPU is not viable. Why aren’t we exploiting that power for other workloads? What prevents a GPU from being a more general purpose computer? What other workloads would benefit from a GPU? Computers are s…
A big one is video encoding. It seems like GPUs would be ideal for it but in practice limitations in either the hardware or programming model make it hard to efficiently run on GPU shader cores. (GPUs usually include separate fixed-function video engines but these aren't programmable to support future codecs.)
- You need to compile shader source/bytecode at runtime; you can't just "run" a program.
- On NUMA/discrete, the GPU cannot just manipulate the data structures the CPU already has; gotta copy the whole thing over. And you better design an algorithm that does not require immediate synchronization between the two.
- You need to synchronize data access between CPU-GPU and GPU workloads.
- You need to deal with bad and confusing APIs because there is no standardization of the underlying hardware.
- You need to deal with a combinatorial turd explosion of configurations. HW vendors want to protect their turd, so drivers and specs are behind fairly tight gates. OS vendors also want to protect their turd and refuse even the software API standard altogether. And then the tooling also sucks.
What I would like is a CPU with a highly parallel array of "worker cores" all addressing the same memory and speaking the same goddamn language that the CPU does. But maybe that is an inherently crappy architecture for reasons that are beyond my basic hardware knowledge.
AMD Strix Halo APU is a CPU with very powerful integrated GPU. It’s faster at AI than an Nvidia RTX4090, because 96GB of the 128GB can be allocated to the GPU memory space. This means it’s doesn’t have the same swapping/memory thrashing that a discrete GPU experiences when processing large models. 16 CPU cores and 40 GPU compute units sounds pretty parallel to me. Doesn’t that fit the bill?
> It’s faster at AI than an Nvidia RTX4090, because 96GB of the 128GB can be allocated to the GPU memory space I love AMD's Ryzen chips and will recommend their laptops over an Nvidia model all day. However, this is a pretty facetious comparison that falls apart when you normalize the memory. Any chip can be memory bottlenecked, and if we take away that arbitrary precondition the Strix Halo gets trounced in terms of…
Why would you normalize though? You can't buy a 96 GB RTX4090. So it's fair to compare the whole deal, slowish APU with large RAM versus very fast GPU with limited RAM.