But how does that apply to GPU ALUs? Looking at M1 die shots, they are comparatively tiny, and when comparing to other vendors, it doesn't seem like Apple is dedicated more logic space to the GPU. The M1 die is roughly 120mm2, an Nvidia Turing TU117 (GTX 1650) is roughly 200m2. Both feature the same amount of GPU ALUs (1024 32-bit units). And of course, M1's 5nm is around 5-6 times denser than Turing's 12nm, but M1 is an entire SoC with all kinds of components — not to mention a huge cache — the GPU takes maybe 20% of the die (let's say 1/3 if you also count in the display controller and memory controllers). All in all, the amount of normalised die space dedicated to GPU ALUs seems comparable.
Of course, my perspective here might be extremely naive, I know very little about semiconductor technology, just trying to understand the principal design differences.
Re:Accelerated PyTorch Training on M1 Mac
by KingOfCoders on Thursday May 19, 2022 @02:35AM
(#31431414)
Also I thought Apple is adding a large slice of cache. When you look at the 3D-V cache on Ryzen for performance (+15%?), this has a large impact. And because they sell expensive stuff, they can afford to build expensive CPUs.
Also related: Apple designs their hardware to do just what they want it to while everyone else is designing for a more general use case. This also costs die area, IP licensing fees etc.