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> Done on special-purpose hardware that is >10 times more efficient than any implementation on GP hardware. That locks users in to only the codecs that have be
by Aardwolf 4y ago
> Done on special-purpose hardware that is >10 times more efficient than any implementation on GP hardware.
That locks users in to only the codecs that have been implemented in this particular hardware, and increases hardware size for particular vendor implementations rather than provide common building blocks that many codecs use (DCT, ...)
> Much better to do on the GPU.
Yep and that's what I run ML stuff on, not all systems have a GPU available though, and for some applications it's faster to do something immediately on the CPU, than have the overhead of going to/from GPU
> In general wide SIMD on CPU has the problem that to make effective use of it, you have to massage your code enough that you are ~95% away from just running it on the GPU anyway,
Better tooling and libraries could help with this imho. Note that the situation for GPU is here also not great at all, since the good tools are locked into 1 vendor.