The Parallella Gen-1 has single precision throughput of 90 GFLOPS, power draw of 5W, and unit cost of $99. This yields 0.91 FLOPS/s per dollar and 18 FLOPS per Watt.

By comparison, the NVIDIA GTX 780 Ti GPU has single precision throughput of 5046 GFLOPS, maximum TDP of 250W, and unit cost of $699. This yields 7.22 FLOPS per dollar and 20 FLOPS per Watt.

This makes the GPU board 8 times cheaper and at least 1.1 times more power efficient than the Parallella board.

Note that the average cost of electrical power in the US is about 10.5 cents per kilowatt-hour (kWh), which means a single GPU board running 24 hours at peak utilization would cost about 63 cents per day - so it would take over 3 years of 24/7 peak operation for the nominal power cost to equal the upfront unit cost. So while power efficiency is very important, that doesn't mean the unit cost can be ignored.

In addition to FLOPS/$ and FLOPS/W, one must also consider the processing power density, which essentially measures how many "FLOPS per cubic foot" are yielded by each device. This is important because these machines take up physical space and physical space (i.e. real estate) is costly. A single GPU board has 56 times the throughput of a single Parallella board, and I would say that a GPU board is only slightly larger than the Parallella board (mainly because GPU boards include massive cooling components which Parallella lacks). A machine that requires 56 times more real estate to achieve the same performance is clearly not competitive.

So far we've only looked at floating-point throughput. One must also consider memory capacity and bandwidth of each device. The GTX 780 Ti has 3GB GDDR5 memory, and I understand that Parallella has 1GB memory. I don't know how the memory bandwidth compares between the two.

It's clear that the Parallella board has a long way to go before it could be competitive for supercomputing applications. And as Parallella tries to catch up, the industry will keep moving - GPUs (and other accelerators, like Xeon Phi) we continue to improve along all these dimensions and I imagine that NVIDIA/Intel/AMD have vastly larger R&D budgets than Parallella/Adapteva. So it is difficult to see how this could ever be a viable supercomputing platform and not just an interesting hobbyist board.