Why do you expect that? Many DBMS operations that are not I/O limited are memory limited, and a GPU does not help you there (on the contrary, you get another bottleneck in data transfers to the small GPU memory). What can help is better data organization, e.g. transposed (columnar) storage.
The more complex the operations performed on the columns (trasnform), the better the GPU database will be - because of the higher ops/bytes ratio
Statistics, sure, that's definitely a good candidate for GPUs. I don't know much about R, but a quick google suggests you can run R code on a GPU, by working with certain object types, like matrices with GPU-accelerated operations.
That doesn't seem like it maps very well to a spreadsheet unless you have one big matrix per cell. I'm guessing (maybe incorrectly) that when people work with matrices in Excel, they're spread across a grid of cells. You probably could detect matrix-like operations and convert them to GPU batch jobs, but it seems very hard and I'm skeptical of how much you'd gain.
So I'm still wondering what kinds of typical Excel tasks are amenable to GPU acceleration in the first place. People use Excel to do a lot of surprising things, sure. But people use C++ and Python and Javascript for a lot of things too, and you can't just blithely move those over to the GPU.
Maybe it's specific expensive operations, like "fit a curve to the data in this huge block of cells"?
Looks like it is indeed identifying large groups of cells with common formulas, and running those calculations on the GPU.
Making very complex tasks run well on a GPU is hard, whereas CPUs are great for dealing with that stuff.
If you have something like a 100,000 row spreadsheet where every row is doing exactly the same calculation on different input data, sure, that starts to make sense. If people are really doing that in Excel, I'm surprised! (but maybe I shouldn't be)