OpenCL is a breakthrough precisely because it enables developers to accelerate the real-time execution of their algorithms quickly and easily — particularly those that lend themselves to the considerable parallel processing capabilities of FPGAs (which yield superior compute densities and far better performance/Watt than CPU- and GPU-based solutions)
There’s still a lot of untapped energy available with the OpenCL programming tools. Apple is still the single largest manufacturer who has adopted OpenCL through a large number of it’s products (OS and App software). And I know from reading about super computing on GPUs that some large scale hybrid CPU/GPU computers have been ranked worldwide (the Chinese Tiahne being the first and biggest example). This article from EETimes encourages anyone with a brackground in C programming to try and give it a shot, see what algorithms could stand to be accelerated using the resources on the motherboard alone. But being EETimes they are also touting the benefits of using FPGAs in the mix as well.
To date the low-hanging fruit for desktop PC makers and their peripheral designers and manufacturers has been to reuse the GPU as massively parallel co-processor where it makes sense. But as the EETimes writer emphasizes, FPGAs can be equal citizens too and might further provide some more flexible acceleration. Interest in the FPGA as a co-processor for desktop to higher end enterprise data center motherboards was brought to the fore by AMD back in 2006 with the Torrenza cpu socket. The hope back then was that giving a secondary specialty processor (at the time an FPGA) might prove to be a market no one had addressed up to that point. So depending on your needs and what extra processors you might have available on your motherboard, OpenCL might be generic enough going forward to get a boost from ALL the available co-processors on your motherboard.
Whether or not we see benefits at the consumer level desktop is very dependent on the OS level support for OpenCL. To date the biggest adopter of OpenCL has been Apple as they needed an OS level acceleration API for video intensive apps like video editing suites. Eventually Adobe recompiled some of its Creative Suite apps to take advantage of OpenCL on MacOS. On the PC side Microsoft has always had DirectX as its API for accelerating any number of different multimedia apps (for playback, editing) and is less motivated to incorporate OpenCL at the OS level. But that’s not to say a 3rd party developer who saw a benefit to OpenCL over DirectX couldn’t create their own plumbing and libraries and get a runtime package that used OpenCL to support their apps or anyone who wanted to license this as part of a larger package installer (say for a game or for a multimedia authoring suite).
For the data center this makes way more sense than for the desktop, as DirectX isn’t seen as a scientific computing or means of allowing a GPU to be used as a numeric accelerator for scientific calculations. In this context, OpenCL might be a nice, open and easy to adopt library for people working on compute farms with massive numbers of both general purpose cpus and GPUs handing off parts of a calculation to one another over the PCI bus or across CPU sockets on a motherboard. So everyone’s needs are going to vary and widely vary in some cases. But OpenCL might help make that variation more easily addressed by having a common library that would allow one to touch all the co-processors available when a computation is needing to be sped up. So keep an eye on OpenCL as a competitor to any GPGPU style API and library put out by either nVidia or AMD or Intel. OpenCL might help people bridge differences between these different manufacturers too.