CUDA only exists because Nvidia is attempting to pretend OpenCL, Vulkan, and DX12 don't exist [1]. These require hardware scheduling on the GPU to switch shaders. Rather then dedicated X amount of chip hardware to Y shader for Z ms.
It should be noted for GPGPU compute Nvidia is not the correct choice. AMD RX 480 has 5.8TFLOPS @$200 ($37/TFLOP) vs Nvidia GTX1080 8.9TFLOPS @$600 ($67/TFLOP). In reality you should be doing GPU programming in OpenCL so you are GPU agnostic. You can switch vendors or platforms seamlessly (in most cases if you avoid proprietary extensions) even target AMD64, ARM, and POWER8/9 hardware.
That being said I own a boat load of Nvidia stock because their marketing is excellent. Really marketing is all 80% of people pay attention too. CUDA has some great marketing around it. In reality CUDA is slower then OpenCL (on Nvidia's platforms even) and no easier to work in.
Let's hope it gets picked up by machine learning frameworks etc., because this market badly needs the competition, as your comparison of per-dollar raw performance numbers shows.
But I'm curious in how the FLOPS on these cards were measured. For example one concern I have is that presumably these two cards have slightly different levels of parallelism. So it may be more or less difficult to extract the full performance from a particular card due to parallelism overhead. Then there's driver overhead, ease of programming, etc.
F * (1/Hz) * 2 = FLOPS
Where Hz is the well.. the clock rate, inverse to get cycles per second. This is wrong because stalls happen, memory transfers, cache misses etc. It is also wrong because the clock rate is throttled and you are not always at Maximum boost clock.
Then multiply by 2 for FMA (fused multiply add). This is wrong because well not every operation is a one cycle FMA. Division can be many (>100). Also scalar pipelines don't have FMA.
Ultimately all vendors use the same crappy calculation so we are comparing apples to apples. Just rotten apples to rotten apples. It gives you a good ideal circumstance you can optimize towards but never actually attain.
Sample applies to integer math, long double math and so on.