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I think this argument is true and it is easily demonstrated with GPUs. They were able to be financially successful products largely because consumers wanted th
by cochne 5y ago
I think this argument is true and it is easily demonstrated with GPUs. They were able to be financially successful products largely because consumers wanted them to play video games.
Now they are necessary for academic research in deep learning and other fields, which would not have gotten GPUs as advanced as they are without the market boost.
Of course that’s just a theory and it’s never possible to go back and see what would have happened. But it seems reasonable to me.
- mirker 5y agoI mean it’s a bit murkier than that: 1. GPUs used to be much less programmable and had fixed-function graphics pipelines. 2. General purpose GPU computing was demonstrated a decade before the 2012 deep learning craze. OpenCL/CUDA was quite mature in 2012 and used by high performance computing. 3. Transistor scaling (Moore’s law) has been speeding everything up exponentially. 4. There are a lot of non-GPU solutions now. If GPUs didn’t exist, someone still could have trained a deep model on a cluster of CPU, and then we’d end up with GPUs (or similar hardware) again. So it’s not necessarily true that gaming->GPU->deep learning implies that GPUs couldn’t be used for deep learning without gaming, because you could have had CPU->deep learning->GPU. I think this was much more of a case of the stars magically aligning than some preset goal by Nvidia/AMD. In fact, there are plenty of cases where you had CPU->research->HW accelerator. One example (on top of gaming/rendering) is compression or parity coding, which was slow initially and then had add-on cards and/or CPU extensions.