We've had 'compute shaders' e.g., https://msdn.microsoft.com/en-us/library/windows/desktop/ff4...
The purpose from this perspective has been to differentiate general purpose computation on GPUs from fixed-function pipelines and/or graphics-specific functionality. The history of using GPUs for general purpose computation involved a lot of hacking to abuse hardware designed for rasterization to do other kinds of calculations.
One keyword / search term you can use is "GPGPU" (which stands for general purpose GPU). Here's another article which might shed more light on the history: https://en.wikipedia.org/wiki/General-purpose_computing_on_g...
* Also found this possibly relevant note: "When it was first introduced by Nvidia, the name CUDA was an acronym for Compute Unified Device Architecture" (https://en.wikipedia.org/wiki/CUDA)
So it seems like a big part of how it's being used is to refer to a generalized computation service—some 'function' you're given access to which takes arbitrary programs as a parameter.
Seems like there's often the implication that how the computation is performed is abstracted over and that more or fewer resources could be applied to it—though that's not necessarily there (absent in the case of compute shaders for instance).
So is the OP:
"We’re releasing an analysis showing that since 2012, the amount of compute [amount of calculation, amount of computation] used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time "
Without loss of meaning, title could be AI and Calculation, or AI and Computation