Here's a graph performance. The GPU version uses NVIDIA's JIT to generate native instructions for a particular GPU so the GPU results here should be more or less the same as if the program was compiled with NVIDIA's static compiler.

So there seem to be several questions as to why people would want to use CUDA when an open standard exists for the same thing (OpenCL).

Well, honestly, the reason why I wrote this was because when I started, OpenCL did not exist.

I have heard the following reasons why some people prefer CUDA over OpenCL:

• The toolchains for OpenCL are still immature. They are getting better, but are not quite as bug-free and high performance as CUDA at this point.
• CUDA has more desirable features. For example, CUDA supports many C++ features such as templates and classes in device code that are not part of the OpenCL specification.

Additionally I would like to see a programming model like CUDA or OpenCL replace the most widespread models in industry (threads, openmp, mpi, etc...). CUDA and OpenCL are each examples of Bulk Synchronous Parallel models, which explicitly are designed with the idea that communication latency and core count will increase over time. Although I think that it is a long shot, I would like to see more applications written in these languages so there is a migration path for developers who do not want to write specialized applications for GPUs, but can instead write an application for a CPU that can take advantage of future CPUs with multiple cores, or GPUs with a large degree of fine-grained parallelism.

Most of the codebase for Ocelot could be re-used for OpenCL. The intermediate representation for each language is very similar, with the main differences being in the runtime.

Please try to tear down these arguments, it really does help.