There is current backlash in the HPC community against GPU/TPU/... aka matrix engine accelerator adoption. Most of the arguments are performance, efficiency and real HPC workload driven.
Like in a recent paper by Jens Domke et Al., colleagues of Dr Matsuoka at RIKEN and AIST in Japan, explore if the inclusion of specialized matrix engines in general-purpose processors are genuinely motivated and merited, or is the silicon better invested in other parts.
I wrote before in that a lot of new HPC systems overuse matrix engine hardware in their architecture. In this paper, the authors looked at the broad usefulness of matrix engines. They found that there is only a small fraction of real-world application that use and benefit from accelerated dense matrix multiplications operations. Moreover, when combining HPC and ML or when you try to accelerate traditional HPC applications, the inference computation is very lightweight compared to the heavyweight HPC compute.
While I agree with the argument put forward some other aspects that go beyond HPC need to be taken into consideration as to why there is such a push for matrix engine adoption. And these aspects are mainly market-driven. If you compare markets, there is significantly more money in the "hyped" years old AI market (training + inference) vs the 30 years old "mature" HPC market.
In raw numbers, the HPC market is worth $39 Billion. In comparison, the AI market is worth $256 Billions in hardware along. If you focus on AI semiconductor only it is still $32 Billion alone! And the growth projections are not in favour of HPC.
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