Performance comparison of CUDA, OpenCL, and C++ AMP

Trying to get information of the underlying design of a GPGPU programming language environment and hardware can be difficult.  Companies will not publish design information because they do not want you or other companies to copy the technology.  But, sometimes you need to know details of a technology that are just not published in order to use it effectively.  If they won’t tell you how the technology works, the only recourse to gain an understanding is experimentation [1, 2].  What is the performance of OpenCL, CUDA, and C++ AMP?  What can we learn from this information?

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C++ AMP

At the AMD Fusion Developer Summit 2011 in June, Microsoft announced C++ Accelerated Massive Parallelism (C++ AMP), an extension to C++ for parallel programming of GPU’s.  This extension, included in Visual Studio 11, would allow developers to program the GPU with language features that are arguably much more powerful than either CUDA or OpenCL.  In comparison, CUDA and OpenCL seem more like stone knives and bearskins. After what seemed like an long three-month wait, Microsoft has finally released the Visual Studio 11 Developer Preview, which contains C++ Accelerated Massive Parallelism (C++ AMP), Microsoft’s vision of GPU programming.  Was it worth the wait?

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OpenCL vs. CUDA

Today's processors have undergone a huge transformation from those of just 10 years ago.  CPU manufacturers Intel and AMD (and up and coming CPU designer ARM) have increased processor speed via greater emphasis on superscalar execution, deeper pipelining, branch prediction, out of order execution, and fast multi-level caches.  This design philosophy has resulted in faster response time for single tasks executing on a processor, but at the expense of increased circuit complexity, high power consumption, and a small number of cores on the die.  On the other hand, GPU manufacturers NVIDIA and ATI have focused their designs on processors with many simple cores that implement SIMD parallelism, which hides latency of instruction execution [1].

While GPUs have been in existence for about 10 years, the software support for these processor have taken years to catch up.  Software developers are still sifting through solutions for programming these processors.  OpenCL and CUDA are frameworks for GPGPU computing.  Each framework comprises a language for expressing kernel code (instructions that run on a GPU), and an API for calling kernels (from the CPU).  While the frameworks are similar, there are some important differences.

CUDA is a proprietary framework. It is not open source, and all changes to the language and API are made by NVIDIA. But, some third-party tools have been built around the framework and it does seem to have a large following in academia.  Unfortunately, CUDA only runs on NVIDIA devices.  While it should be possible to run CUDA code on other platforms using Ocelot, this only works on Linux systems.
 
OpenCL is a standardized framework, and is starting to gain popularity.  Similar to NVIDIA's CUDA C++, OpenCL allows programmers to use the massive parallel computing power of GPU's for general purpose computing.  Unlike CUDA, OpenCL works on any supported GPU or CPU, including Intel, AMD, NVIDIA, IBM, and ARM processors. 
 
Does OpenCL make programming multiple platforms easier?  Is it as fast as CUDA, or does it sacrafice speed for diverse platform support?

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