OpenCL: Parallel programmers' new best friend

OpenCL, created to support parallel programming in Apple's Snow Leopard operating system, may become the new world standard for parallel programming on all platforms.

Peter Glaskowsky
Peter N. Glaskowsky is a computer architect in Silicon Valley and a technology analyst for the Envisioneering Group. He has designed chip- and board-level products in the defense and computer industries, managed design teams, and served as editor in chief of the industry newsletter "Microprocessor Report." He is a member of the CNET Blog Network and is not an employee of CNET. Disclosure.
Peter Glaskowsky
3 min read

Apple's Snow Leopard operating system, which hits the streets on Friday, has plenty of new technology--but one of its major new features will soon be available on Microsoft Windows, Linux, and other major platforms.

OpenCL, the Open Computing Language, was originally proposed by Apple to support parallel programming on GPUs. There are other GPU programming languages, such as Nvidia's CUDA (Compute Unified Device Architecture) extensions for C and the Brook stream program language developed at Stanford University and included in Advanced Micro Devices' Stream Computing software development kit, but rather than choosing one of these languages, Apple chose to create a new standard independent of the big graphics vendors.

In fact, OpenCL is even independent of Apple. One of the first things Apple did was offer to hand it over to the Khronos Group, the same independent standards organization that manages the OpenGL standard for 3D rendering.

OpenCL working group member logos
Supporters of the OpenCL standards effort at the Khronos Group include the biggest CPU and GPU makers in the industry. Apple is also involved but not shown here.

The members of the OpenCL working group turned Apple's draft specification into the released version 1.0 spec in just six months (see Brooke Crothers' "OpenCL goes beyond Apple" from last December)--and in the process, it created what may be the best solution so far to the general problem of parallel programming.

See, OpenCL isn't just for GPUs. It was designed from the beginning to get the most out of multicore processors too. After all, if you have a multicore CPU--and you probably do--why let it go to waste? OpenCL is flexible enough to support both CPU-optimized and GPU-optimized code, and smart enough to choose the right code, depending on what hardware is available in the system to run it. Most of the competing parallel-programming languages can't do that.

OpenCL can take advantage of both task-level parallelism (running many tasks at once, whether different tasks or copies of the same task) and data-level parallelism (where a single instruction within a task is applied to multiple data items at once--also known as SIMD). Some parallel-programming languages can't do that, either.

But OpenCL's biggest advantage isn't technical in nature: it's that no other parallel-programming language will be so widely supported. The support starts with Snow Leopard but will go well beyond that. AMD and Nvidia will have OpenCL drivers for their GPUs under Windows and Linux. AMD and Intel will support OpenCL on their CPUs (including Intel's Larrabee). And AMD has already shipped its first OpenCL implementation for its Athlon and Opteron processors.

Implementations for video game consoles and DSPs (digital signal processors) are also under development. I've even heard that future releases of OpenCL may be able to work with less common hardware, such as FPGAs (field-programmable gate arrays).

We had an excellent half-day OpenCL tutorial last weekend at Hot Chips 21. There were also some great OpenCL presentations at Siggraph 2009 earlier this month; if you'd like more detailed information, that's a good place to start.

All this support for OpenCL means that it should become the first choice of academic and commercial developers who want a good cross-platform way to develop parallel code. Expect to see OpenCL used in software for audio and video processing, cryptography, medical imaging, and many other applications--including, of course, gaming.

(Disclosure: I will be writing a technical white paper for Nvidia, one of the companies covered in this story.)