[mattkrause]

There is CU2CL: http://chrec.cs.vt.edu/cu2cl/

The cross-platform nature is actually part of the problem--the whole point of doing GPGPU work is that you're playing to the hardwares' strengths, which can be difficult when the hardware can be nearly anything from a CPU to a GPU to an FPGA.

It doesn't help that until recently, AMD hasn't tried to push OpenCL nearly as hard as nVIDIA pushes CUDA.

[Athas]

Modern AMD and NVIDIA GPUs are fairly similar hardware-wise, and it is not hard to write OpenCL code that executes efficiently on both. I agree that it is pretty hopeless to write performance-portable OpenCL across entirely different architectures, however.

[mattkrause]

Sure, but if you go with nVIDIA, you also get access to all the other goodies they distribute (thrust, cudaFFT, cudaDNN, etc) and all the CUDA-compatible stuff other people have written, like Theano and TensorFlow.

It does seem like people have gotten a little more interested in OpenCL lately, but it still lags pretty far behind. As dharma1 says below, AMD seems weirdly uninterested in catching up. If I were in change of AMD, I'd be throwing money and programmers at this: "Want to port your library to OpenCL? Here, have a GPU! We'll help."

[dharma1]

AMD management has completely missed the memo on deep learning. No mention of deep learning or FP16 perf yesterday when Polaris was announced - it was all around VR.

They are just not turning up to the party and as a company are running out of time if Polaris and Zen dont sell.