I'm looking for some introductory examples to OpenCL which illustrate the types of applications that can experience large (e.g., 50x-1000x) increases in speed. Cuda has lots of nice examples, but I haven't found the same thing for OpenCL.
A nice example might be global optimization of complex functions via particle swarms, simulated annealing, evolutionary algorithms, ant colony optimization, etc.
A study that directly compared CUDA programs with OpenCL on NVIDIA GPUs showed that CUDA was 30% faster than OpenCL.
CUDA and OpenCL offer two different interfaces for programming GPUs. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs.
OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs. Using the OpenCL API, developers can launch compute kernels written using a limited subset of the C programming language on a GPU.
CUDA being a proprietary NVIDIA framework is not supported in as many applications as OpenCL, but where it is supported, the support makes for unparalleled performance. While the OpenCL which is supported in more applications does not give the same performance boosts where supported as CUDA does.
The algorithms you are describing are neither simple nor introductory from the perspective of GPU programming. The reason CUDA has examples in these areas is that it has been around long enough for people to have developed these examples. There is currently no publicly available version of OpenCL that runs on GPUs. Both ATI and NVIDIA are offering beta versions of their OpenCL drivers, but ATI's supports only CPU computation and NVIDIA's requires signing an NDA to get. Simply put, OpenCL has not been around long enough for comprehensive examples like these to have been developed and demonstrated.
That said, gaining access to NVIDIA's OpenCL drivers is not difficult. You can find out how to do so on their forums here. I assume that the OpenCL distribution contains some sample programs to help you get started.
This also means that it's an excellent opportunity for you to develop some of these benchmarks and post your results. Then people will refer to your work rather than you referring to their work. I wouldn't expect too many surprises though. OpenCL performance should be roughly on par with CUDA performance once it becomes widely available and supported.
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