[bisrig]

I think the heart of what the article is getting at is represented well by the following quote:

"To let GPUs blossom into the data-parallel accelerators they are today, people had to reframe the concept of what a GPU takes as input. We used to think of a GPU taking in an exotic, intensely domain specific description of a visual effect. We unlocked their true potential by realizing that GPUs execute programs."

Up until the late 2000s, there was a lot of wandering-in-the-wilderness going on with respect to multicore processing, especially for data-intensive applications like signal processing. What really made the GPU solution accelerate (no pun intended!) was the recognition and then real-world application (CUDA & OpenCL) of a programming paradigm that would best utilize the inherent capabilities of the architecture.

I have no idea if those languages have gotten any better in the last few years, but anything past a matrix-multiply unroll was some real "here be dragons" stuff. But: you could take these kernels and then add sufficient abstraction on top of them until they were actually usable by mere humans (in a BLAS flavor or even higher). And even better if you can add in the memory management abstraction as well.

Point being: still not there for FPGA computation, though there was some hope at one time that OpenCL would lead us down a path to decent heterogeneous computing. Until there's some real breakthroughs in this area though, the best computation patterns that are going to map out using these techniques are the things we're already targeting to either CPUs or GPUs.