Well, that's about as many as a modern 3D game engine executes in one second, so, meh.
It's a complete apples to oranges comparison. Scribd is performing quite complex logic with an emphasis on correctness not speed. I feel like you're dismissing an interesting article for the sake of an amusing one liner, and that's pretty much the antithesis of HN.
It is a nicely-written and illustrated article, but it describes a problem that hasn't been considered 'interesting' for decades and can, in any case, be tackled with a caching scheme. Why would they need to run the same intersection logic over and over, when there's only a finite number of glyphs to render in any given document?
Actually, polygon operations on grids is still an active research area, with the latest papers on the topic less than two weeks old: http://www.sci.utah.edu/socg2010agenda.html
> Why would they need to run the same intersection logic over and over [...]?
We usually don't. It basically depends on the context in which the glyphs appear on the page. For a standard, say, LaTeX document consisting of mostly text and without weird graphic operations taking place around or on top of the text, a given glyph is just processed once.
1) GPU development requires specific developer experience. Making performant GPU code is an even nastier and less intuitive problem than making performant CPU code. A naive implementation would be shockingly inefficient.
2) Leasing GPU hardware in a datacenter is very rare. You'd have to do it at the office, or build your own servers and install them yourself at a colo. Lots of time and effort.
Even if the GPU solution was 10x faster (it could be much more, but it depends on how much the CPU, disk, network is a bottleneck), if you're talking about reducing $50k in computer time to $5k in computer time, it's almost certainly not worth it. If you're talking about $2m to $200k, that's a completely different matter.
This could be very useful for running certain algorithms in web apps...
Below I've linked github urls and two blog posts that were done about them. Unfortunately right now webGL is kind of a moving target and I doubt these examples still work out of the box. However, if you are interested in GPGPU on webGL I would encourage you to get involved as I think there are some tweaks to the standard that would make GPGPU life easier.
Matrix Mutliply http://learningwebgl.com/blog/?p=1828 http://github.com/bunions1/matrixMultiplyGpu
Falling Sand http://learningwebgl.com/blog/?p=1471 http://github.com/bunions1/fallingsand-webgl
The important bit is at the end where they say "10% of the time is spent on polygon intersection" and the rest on image processing. We do GPU image processing on the cloud and this is (likely) where Scribd could likely obtain a significant speedup - many of our image processing operations run hundreds of times faster than on the CPU.