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>Noone does parallel compute on CPUs these days, not since GPGPUs rocked up almost 5 years ago (and we often use python as the host language, thanks pyCuda!)

You'd be surprised. Furthermore, I call BS.

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Who can afford to throw CPUs at parallel compute problems today with GPGPUs available? Oil and Gas industry? Nope, the 3 biggest are nVidia customers in a big way. Finance? Nope, some of the smaller companies here have stepped past even GPGPUs and are now co locating FPGAs in the exchanges. Big pharma? Not that I know of, also onto GPU clusters in the 2 big cases I know there.

So yes I would be surprised. Surprise me?

Calling BS on?

Calling BS based on the fact that the VAST majority of people doing work with Python use Numpy and similar tools, and not GPUs based for their work.

And it's not the "oil and gas industry" or finance, which might have been your expertise and might use GPUs, but are nowhere near being even a large minority of Python use.

It's scientific computing. This is Python's largest niche that needs to parallel compute problems.

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