OpenCL – the battle, part II

Part II: the software-companies

It is very clear what’s at stake for the hardware-companies; we’ve also discussed the operating systems. But what should the software companies do? For companies which make i.e. encoding-software or databases it is very simple: support OpenCL or be years behind (what marketing can’t fix). For most other software there is a dependency on the programming language since OpenCL is a very specialised way of programming which (most times) is too different from in-house knowledge and can therefore be too expensive.

This article is somewhat brief, since most of the material will be discussed further in later-to-be-released articles.

Video-encoding and rendering

Why we had easily 60 frames per second in games but rendering an image of our own house would take minutes? You had the feeling there was a gap between worlds which needed to be closed. OpenGL/DirectX did a lot (also see our next article about OpenGL, OpenCL and DirectX), but was not able to help us in outside games. Apple did a lot to the desktop by integrating hardware-acceleration (later copied by Linux and Windows), but somehow GPU-processed results were not regarded professional and maybe seen more as an intermediate result (to see how it would look like).

Elemental Technologies was first with its H.264/AVC encoder; Nero and nVidia joined forces somewhat later. Both are based on CUDA and not OpenCL. Since rendering is close to what we already expected to come out of a GPU, we think this market is very soon recovering introducing the same product, based on OpenCL.

A few months ago nVidia has released its GPU-based ray-tracing engine, OptiX. On Youtube you can find the demo of VRay‘s accelerated ray-tracing engine.

We expect a lot of news from the graphics-world, since they already know how to program with shaders. A lot of artists will love the free speed-up, but it’s not breaking news this would be possible.

Programming languages

C and C++ are official bindings of OpenCL. And thereby Objective-C (used on i.e. on the iPhone) has native support.

As we described last week, we think that Oracle/Sun is taking OpenCL more serious now, Several wrappers exist for Java, but native support is missing; we would suggest writing the OpenCL-part in C or C++ when using Java, even if this breaks the beauty of the multi-platform-language.

It is very clear Microsoft had a better view with being an early adopter with Visual Studio integration trough profilers (created by AMD and nVidia). You already see higher-level implementations, such as the C#-toolkit OpenTK has included support that goes beyond the default dll-bindings. Also here programming parts in native C would be best.

Python is famous for its endless wrappers around anything, so it was to be expected to find an OpenCL-binding. Python has always been the safe choice for scientific programming, because of its enthusiastic community.

A binding for OpenCL in languages like PHP and Perl is completely absent. Most times this is not a problem, as C-libraries can easily be called.

RapidMind had en product which provided higher-level programming on the GPU, but after its acquisition by Intel, we don’t see the product any more. So we can conclude we just have to wait for native support in other languages than C, C++ and objective-C, to have better support.


We will cover databases later, when projects are more mature. In short, currently is investigated how GPUs can do, what SUN’s UltraSparcs already did. Since the memory-bandwidth is only great when using the onboard-memory, this is not as promising as it looks. Index-searches can be sped up, but these are not the real bottle-neck in database-performance. We think it is very important to invest in OpenCL-research in this competing market.

Operating Systems

Apple has had good GPU-support since OSX and therefore a good understanding of graphic-cards. Apple started the project OSX and already has updated several core libraries with OpenCL.

Microsoft has built DirectCompute in DirectX 11. This is OpenCL-technology put in a MS-jacket, as we’ve seen the company do many times before. Coming up is an article which discusses the differences.

Linux (Desktop and HPC) have not great support for OpenCL, but it works well enough to have most large OpenCL/CUDA-upgraded clusters on its name. Due to the flexibility of the OS and the strong competition between nVidia and AMD, a lot of research is done. Nevertheless there are no core-libraries in Linux which support OpenCL. We expect i.e. visualisation-libraries to support OpenCL this year.

Mathematical software

Matlab, Octave, Mathematica, R and Maple will all have a big advantage by using the GPU. Matlab has the most support by external libraries: CUDA, Jacket, gpuMat, etc. Mathematica will soon release a CUDA-version of Mathematica. R is still in discussion, Octave has a few partial/abandoned implementations of some libraries; since there is a lot of money to make by selling these products we can only expect full open-source implementations. Maple refers to its external call routines, so we still have to wait a while until we can have GPU-support.


This short overview gives an idea of where to expect to find OpenCL-powered solutions. When we find more markets the coming weeks, we’ll update this post.

Clear winners cannot be pinpointed, since the door has just opened. Maybe Nero, since it will now sell more of its encoding-products to owners of nVidia GPUs.

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