CUDA was released, supported by NVIDIA GPUs, in early 2007. The first OpenCL specification was not released until late 2008 (OpenCL has not been around for 4 years, as you claim). As for which is more popular, I'm afraid that you have this backwards too. The dominant market force for GPU computing is supercomputing. How many of the top 5 supercomputers used AMD GPUs? Zero. How many use NVIDIA GPUs? Three. And they're all using CUDA because it's more feature rich---it can do fancy things like direct memory copies between infiniband interconnects and GPU memory.

FYI: OpenCL on NVIDIA is implemented on top of CUDA, so you're still using CUDA if you're using OpenCL on NVIDIA.

Surprise, surprise, I have the feeling that most of you haven't actually read the article. The article is not arguing that GPUs are inherently flawed. Also, the article is not an NVIDIA-vs-AMD competition. Rather, the author tests software on each platform. It's the software that is bad, not the GPUs themselves. For instance, the NVIDIA GPU does quite well with Arcsoft and Xilisoft; this wouldn't be possible if GPUs were somehow broken for transcoding. After all, as others have pointed out here, floating point support is actually quite good on modern GPUs.

Still, poor software shouldn't come too much as a surprise. While CUDA and OpenCL certainly make GPU-based computing easier, it is still a relatively new technology that only a few programmers know how to use efficiently. I'm also not sure that the market pressure is there yet from consumers for efficient GPU-based applications (how many of them actually know what a GPU is?).

I suppose this is an improvement over a design from another Dutch firm for residential towers in South Korea: http://www.dailymail.co.uk/news/article-2072308/MVRDV-architects-reveal-plans-South-Korean-buildings-look-eerily-like-Twin-Towers-exploding.html

SLI is absolutely useless for CUDA-based (Cray's uses NVIDIA GPUS) GPGPU.

Well, Apple, it looks like you'll be the last major OS still running a terribly out of date file system. Ditch HFS+!

I think the time of the PS3 clusters has past. The Cell processor was released back in 2006! IBM released a few upgraded processors, mostly improving double-precision performance, but those systems are really cost prohibitive.

Assuming you can deal with PCIe latency, GPUs are the way to go.

What does SLI give you in CUDA? The newer GeForce cards support direct GPU-to-GPU memory copies, assuming they are on the same PCIe bus (NUMA systems might have multiple PCIe buses).

My research group built this 12-core/8-GPU system last year for about $10k: http://tinyurl.com/7ecqjfj

The system has a theoretical peak ~9.1 TFLOPS, single precision (simultaneously maxing out all CPUs and GPUs). I wish the GPUs had more individual memory (~1.25GB each), but we would have quickly broken our budget had we gone for Tesla-grade cards.

If you are already going to be in New Mexico to see the Very Large Array, try to swing by the Carlsbad Caverns: http://www.nps.gov/cave/index.htm

Sure, it's not tech-oriented, but I'm sure you can get your geology geeking on. It's not often one is in the area (BFE New Mexico), so take the opportunity. The caverns are not to be missed!

Interesting projects, but how do they get funding?

Thought: Would you rather own the spider or a spyder?

Let's make this a record level of comments for a Slashdot story.

Goodbye, Steve. It was with a Mac that I came to love computers.