I have not tried it for two reasons. First, to my knowledge there are no large public machines in the US being planned using AMD GPUs, so there is relatively little incentive to port the code to OpenCL. We run on large clusters and it appears for the moment that NVIDIA has the HPC cluster market tied up. Second, while OpenCL is quite similar to CUDA in many respects, it's also significantly less convenient from a coding perspective. NVIDIA added a few language extensions that makes launching kernels nearly as simple as a function call. As a pure C library, OpenCL requires much more setup code for each kernel invocation. If there was a strong incentive, such as the construction of a large NSF or DOE machine with AMD GPUs, I'd probably port it anyway, but without such a machine, it's not worth the time and effort.
It's important to note that on GPUs, peak performance data often doesn't translate into actual performance numbers. The 4870 had a higher peak floating point rate than the G200, but in graphics and some other benchmarks, the G200 usually came out ahead. I don't know if this will also be the case with Fermi vs. 5870's.
Finally, another large consideration is that AMD is pretty far behind on the software end. Besides mature compilers for both CUDA and OpenCL, NVIDIA provides profilers and debuggers that can debug GPU execution in hardware, and there is a growing ecosystem of CUDA libraries. For the sake of competition, I hope AMD adoption grows, but I've gotten the impression they are just not investing that much in general-purpose GPU computing.