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Microsoft says “Prism” translation layer does for Arm PCs what Rosetta did for Macs
(arstechnica.com)
this post was submitted on 20 May 2024
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On the x86 architecture, RAM is used by the CPU and the GPU has a huge penalty when accessing main RAM. It therefore has onboard graphics memory.
On ARM this is unified so GPU and CPU can both access the same memory, at the same penalty. This means a huge class of embarrassingly parallel problems can be solved quicker on this architecture.
Do x86 CPUs with iGPUs not already use unified memory? I'm not exactly sure what you mean but are you referring to the overhead of having to do data copying over from CPU to GPU memory on discrete graphics cards when performing GPU calculations?
Yes unified and extremely slow compared to an ARM architecture’s unified memory, as the GPU sort of acts as if it was discrete.
Do you have any sources for this? Can't seem to find anything specific describing the behaviour. It's quite surprising to me since the Xbox and PS5 uses unified memory on x86-64 and would be strange if it is extremely slow for such a use case.
It’s been a while since I’ve coded on the Xbox, but at least in the 360, the memory wasn’t really unified as such. You had 10 MB of EDRAM that formed your render target and then there was specialised functions to copy the EDRAM output to DRAM. So it was still separated and while you could create buffers in main memory that you access in the shaders, at some penalty.
It’s not that unified memory can’t be created, but it’s not the architecture of a PC, where peripheral cards communicate over the PCI bus, with great penalties to touch RAM.
Well for the current generation consoles they're both x86-64 CPUs with only a single set of GDDR6 memory shared across the CPU and GPU so I'm not sure if you have such a penalty anymore
Are there any tests showing the difference in memory access of x86-64 CPUs with iGPUs compared to ARM chips?
That’s actually not what I was referring to, although the unified memory architecture is certainly more power efficient for mixed-intensive workloads. The cost of transferring to/from dedicated GPU memory is (unsurprisingly) quite large.