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It depends on the domain. Increasingly people's interfaces to this stuff are the higher level libraries like tensorflow, pytorch, numpy/cupy, and to a lesser de
by mikepurvis 3y ago
It depends on the domain. Increasingly people's interfaces to this stuff are the higher level libraries like tensorflow, pytorch, numpy/cupy, and to a lesser degree accelerated processing libraries such as opencv, PCL, suitesparse, ceres-solver, and friends.
If you can add hardware support to a major library and improve on the packaging and deployment front while also undercutting on price, that's the moat gone overnight. CUDA itself only matters in terms of lock-in if you're calling CUDA's own functions.
- bigcat12345678 3y agowhat I meant is that all these stuff have 15 years of implicit accumulation of knowledge and tips and even hacks builtin in the software No matter what you depends on, you'll have a slew of larger or minor obstacles or annoyance That collectively is the most itself As you said, already it's clear that replacing cuda itself is not that daunting