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[D] Difference between CUDA and Tensor Cores (alien.top)

submitted 11 months ago by 3DHydroPrints@alien.top to c/machinelearning@academy.garden

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CUDA cores (or shader cores in general) have long been used to compute graphics. A very often used operation in computer graphics are matrix multiplications, just like in deep learning. Back in the days (AlexNet) NNs were computed using shader cores, but now have completely moved to be computed on Tensor cores. My question are:

Why have these workloads been seperated? (Yes obviously the tensor cores are more specialized and leave out a bunch of unnecessary operations, but how and why not integrate it into the CUDA cores to boost MM operations for computer graphics?)
Why isn't the workload offloaded to the other cores when the mathematical operations are the same
What makes tensor cores so much more efficient and faster?

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[–] smt1@alien.top 1 points 11 months ago (2 children)

I'd listen to this recent talk by Bill Dally (chief scientist @ NVIDIA) who talks a bit about the underlying math operation primitives from a computer architecture-level:

https://www.youtube.com/watch?v=kLiwvnr4L80

He cuts through some of the marketing language like CUDA and tensor cores to focus on the complex instructions from 16 mins on or so.

[–] ognjenivuk@alien.top 1 points 11 months ago

!remindme 1day

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