I get the idea you are aiming for, but this will not work.

GPUs are vector processors. They are good at certain types of repetitive unrelated (parallel) math. They can be good at simulating physics since physics is described with matrix math.

You could simulate the gates in a chip, though this is a MASSIVE problem and will tax the GPU for meh results. If you decided to simulate a chip in a GPU, simulating an FPGA would be quite pointless since FPGAs are less than optimal so they can be reconfigured to implement (not simulate) different chips.

This would be like running a Sparc emulator in a Java VM implementation of an Intel VM... mabe throw javascript in there somewhere. Each level of emulation, simulation, or implementation robs resources.

Yes those architectures are more open but they do not do the same thing. More work with those setups so, say, Postgres can offload certain math to the GPU, is great and should be done. But this is somewhat different than FPGAs (though in the same spirit).

CPUs, GPUs, and FPGAs all solve different types of problems very well. But they do not run each other's problems well, and certainly do not implement each other super well. Well, with one exception: you can build a reasonable GPU with shaders in an FPGA, but this is not to out perform the FPGA it is implemented on in a task. If you hardwired the FPGA to run the equivalent operations of a shader in OpenCL running on a GPU implemented in the same FPGA, the FPGA would win hands down.

I can't see how you could possibly create an FPGA from a GPU, whereas you could/can create a GPU from an FPGA, for example Altera have OpenCL running on FPGAs, and also people have implemented GPUs using an FPGA.

Note that Altera's compiler for Open CL does not let you run your OpenCl stuff faster. It is intended so you can write your FPGA programs (that run outside of your computer usually) in OpenCL. Most of the imagined benefits kind of vanish, at least to me, when you realize that this is just a tool so if all you can hire if OpenCL developers, you can still get the job done.

Things like GPUs and Parallella are great, but it would still be great to have an open FPGA architecture. Some problems don't map all that well into a GPU, but would be fairly simple to map into an FPGA to get huge performance increasese. Also, a GPU comes with a computer and that tends to be large and power hungry. Often times an algorithm can be implemented in an FPGA which can run on 5W and fit into a package the size of a deck of cards.