Recieved my previous payment just fine. Would like to participate again too. My sig is correct, yes?

The problem here is you aren't dealing with absolutes.  On SHA-256 it takes ~72MH per minute average to solve a difficulty 1 hash.  The chains found by Primecoin are random and as ypool found out, some people could tweak to forego the longer chains and concentrate on getting mass quantities of the 6-ch, basically stealing from the honest block finders.  You could have 1000 identical CPUs running the exact same setting and see a variance unheard of in SHA-256 processing.  In unscientific terms, it's the 'luck' factor.

im pretty sure you fucked up, and shouldnt be blaming ypool for your own mistakes, there is no way you only found .01 xpm in a whole day of mining without screwing something u.

CUDA would work very well for this type of computing.  Over on Mersenne.org, they have had CUDA based programs to run the Lucas-Lehmer tests for quite some time now while the OPENCL crowd have barely gotten one functioning and at nowhere near the speed of CUDA.

In trial factoring work, a GTX590 using CUDA puts out 681.6GHz Days of work per day compared to a 7990 using OPENCL putting out 748.7.   On sha-256 the 590 is ~190 to the 7990's 1200+.   Porting the OPENCL to CUDA will not be an easy task, but I'd bet the result would surprise you.

Yeah, Apple owns the trademarks because they're the ones who brought everyone to the table.  Apple loved CUDA but isn't dumb enough to sole-source any of their parts.  So they told NVIDIA and ATI that they should all play nice and standardize CUDA.  OpenCL was the result.  It's only barely different from OpenCL.  The biggest differences are primarily in making CUDA fit a programming model similar to the shaders already used in OpenGL.  NVIDIA wanted to win a contract with Apple and they had a huge headstart on the competition.  AMD's Brook and CAL/IL was mostly a flop, so they would happily jump onboard with a Khronos standard.

If you look just at hashing (and now prime number computation), you're missing a much bigger part of the GPGPU marketplace.  Most of the GPGPU customers (in terms of units purchased) are running floating point computations of enormous matrices and using the interpolation hardware.  They're used in scientific applications, Oil&Gas, Medical stuff, etc.  In those applications, NVIDIA does very well, often better than AMD.

Wha..?!  No way!  NVIDIA has a huge advantage over AMD in many aspects.  Just look at how well their software works compared w/AMD's.  You still need an X server running to do computation with AMD GPUs and that totally blows.

NVIDIA made a poor (IMO) strategic decision by abandoning OCL but you still have to give them the credit for creating it!  I think they were afraid to abandon their early adopter CUDA customers and decided they didn't have the throughput to support both.

I think eventually they'll reverse their position on OCL.  But to a lot of folks doing GPGPU they don't care about OCL and they're using CUDA and loving it.  So it's not fair to say "NVIDIA is poor at doing anything GPGPU" IMO.

Any word on getting this to work on NVIDIA cards? From what I understand it's because the nvidia cards don't support opencl 1.2 (yet?). Any potential workarounds on windows or linux?

I'd be offended if I was offered only $27/hour for a professional job.