HPCwire: Compilers and More: OpenCL Promises and Potential

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September 10, 2009

Compilers and More: OpenCL Promises and Potential

by Michael Wolfe, Compiler Engineer, The Portland Group, Inc.

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OpenCL was introduced with great fanfare and promise. Apple has served a key role, perhaps the key role, in pushing OpenCL to ratification and public release. In fact, Apple has apparently filed for trademark protection on OpenCL technology, presumably (or hopefully) to prevent anyone else from claiming prior art and thereby preventing Apple from using it themselves. The development of OpenCL included dozens of industrial and institutional partners, including AMD, ARM, IBM, Intel, NVIDIA, and Texas Instruments.

The excitement is intensified by a quote from Steve Jobs saying, "It's way beyond what NVIDIA or anyone else has, and it's really simple," (more on this later), and the first line on the Khronos OpenCL page: "OpenCL is the first open, royalty-free standard for cross-platform, parallel programming of modern processors found in personal computers, servers and handheld/embedded devices." That last comment leaves me rankled, since I'm personally deeply involved in OpenMP, another open, royalty-free standard for parallel programming of modern processors; perhaps there are enough adjectival phrases in the OpenCL statement to distinguish it. At the SC08 OpenCL Technical Briefing, Tim Mattson from Intel, who is also deeply involved in OpenMP language development, gushed about how we now finally have a portable parallel programming model, and predicted that half the people in the room would be using OpenCL by the next SC conference. I guess we'll see in just a couple of months.

So, given all the hype, what can we expect from OpenCL? Is it really simple? Is it portable? Will it replace other parallel programming models? It's still a little early; we've seen a multicore demonstration of OpenCL from AMD, a limited developer release from NVIDIA, and Apple is planning to release its next generation operating system in September, including OpenCL support. Yet we can prognosticate, given what we know about the language and related technologies.

Is it simple?

Simple is a loaded term. One can claim anything is simple if it's simpler than something else that's even more complex. But I've said before that parallel programming is hard, and is going to remain so. Perhaps that's overstating the case: we can probably make it easy to write parallel programs that perform badly, but I'm assuming that isn't the intent and is obviously unacceptable.

So, is there a metric by which we can claim that OpenCL is simple? I'm going to give a qualified no. In OpenCL, you have a host and one or more compute devices; you have a host program that launches kernels; you have task parallelism and data parallelism, work-items and work-groups; you have contexts and command queues; you have global memory, local memory, and private memory, not to mention constant memory, with different consistency models across the memory types; you have buffer objects, image objects, and sampler objects; and you have language restrictions and language extensions. It's not simple.

Over fifty years of programming language development has led us to expect certain features in our programming environment, such as simple access to modular programming (procedures, argument passing, linkers), which are not so well represented in OpenCL. There's no linker for GPU kernels in OpenCL; you must include library calls to build a device program and extract a handle to the kernel function. There's no direct function call to the kernel; you must set up the arguments one at a time with a series of library calls. The OpenCL strategy was to not perturb the host programming language with additional keywords or syntax, but to encapsulate additional functionality in a host-side library. You can expect any number of OpenCL preprocessors to convert a higher-level representation (a la NVIDIA's CUDA) to the lower-level OpenCL. If I haven't convinced you that OpenCL isn't simple, look at an OpenCL example program, in this case to compute the sum of a vector of numbers (provided by Apple).

So, what's my qualification? If you had looked at the hoops programmers had to jump through to program GPUs for general purpose computing before OpenCL and CUDA, you'd agree that these languages are a very large step in the right direction. Much simpler, though not really simple.

Is it portable?

Again, we should define our metric. We've grown accustomed to being able to write programs that will run across a wide variety of target systems with almost no modifications. Compiled languages (Fortran, C, C++) will require a recompile for a new processor or operating system, and may need changes for system calls. Just-in-time platforms (for Java, C#) and interpreted languages (Perl, Python) don't even need that.

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