Introduction, GPGPU history, ATI Stream and CUDA overviews

Since our initial review of five of NVidia’s CUDA-enabled applications back in June, we’ve been chomping at the bit to get our first real look at ATI’s entry into the GPU computing ring called ATI Stream. Both of these platforms use parallel computing architectures to utilize their GPU’s stream processors, in tandem with the CPU, to significantly increase any system’s video transcoding speeds. Today, we are going to discuss both of these technologies as well as benchmark a couple video transcoding applications from Cyberlink that actually support both CUDA and ATI Stream.

It’s a bit late to the party, but can ATI Stream bring the heat against a refined CUDA technology?

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Since our initial review of five of NVidia’s CUDA-enabled applications back in June, we’ve been chomping at the bit to get our first real look at ATI’s entry into the GPU computing ring called ATI Stream. Both of these platforms use parallel computing architectures to utilize the GPU’s stream processors, in tandem with the CPU, to significantly increase any system’s video transcoding speeds.

Today, we are going to discuss both of these technologies as well as benchmark a couple video transcoding applications from Cyberlink that support both CUDA and ATI Stream platforms. We will also take a  brief look at ATI’s Avivo video converter to see what ATI’s own free software has to offer.

 

GPGPU history at a glance

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Video Equipment with Ikonas graphics system (Courtesy photo)

The first General-Purpose Graphics Processing Unit or GPGPU was initially created in 1978 when Ikonas developed a programmable raster display system for cockpit instrumentation. Before 2006, there were only a handful of other systems that incorporated GPGPU technology.

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Former CEO Dave Orton explains ATI’s Stream computing initiative at a press event in 2006 (Courtesy of TechReport.com)

In November 2006, AMD’s website stated they started the “GPGPU revolution” with the introduction of “Close To Metal”, the first iteration of their GPGPU technology that has now evolved into ATI Stream. But, after several missteps and delays, they weren’t actually able to fully utilize ATI Stream technology until their December 2008 launch of the ATI Catalyst 8.12 driver, which officially brought Stream to the masses.

To give consumers a glipse of this new technology, AMD reconfigured ATI’s free Avivo Video Converter to be Stream-compatible. Since it’s re-release in 2008, only two video transcoding applications have incorporated ATI Stream into its programming — Cyberlink’s PowerDirector and MediaShow Expresso applications. There are other applications in various stages of development, but nothing else available on the market currently.

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NVidia CEO and president Jen-Hsun Huang plays with a game using NVidia’s Physx technology for gaming, at the International Consumer Electronics Show in Las Vegas Jan. 8, 2009. (Courtesy photo)

On the other side of the fence, NVidia’s Compute Unified Device Architecture or “CUDA” platform was announced together with G80 in November 2006. A public beta version of the CUDA SDK was released in February 2007. The first version of CUDA rolled out with Tesla in June 2007, which was based on G80 and designed for high performance computing. At the end of 2007, NVidia released CUDA 1.1 beta, which added new features but was a minor release. Since it’s initial release, CUDA has been used and featured in seven retail video transcoding applications.

The development of GPGPUs is truly about fully utilizing all the processing potential that lies dormant in graphics cards when users aren’t playing Crysis or Far Cry 2. GPGPUs will allow users to see what will happen if other applications are able to make use of the stream processors in a graphics card. This is why NVidia and AMD are frantically working to harness the GPGPU potential of their respective graphics hardware.

 

Why is GPGPU technology important?

The importance of the emergence of GPGPU technology is simple — it will increase the speed of many types of tasks consumers do every day by using the GPU and the CPU in tandem for “general purpose” computations (or number crunching) that was once only handled by the CPU alone. When this technology fully matures, consumers will see noticable performance increases when they convert audio and video files, play graphics-intensive games, and in other daily tasks. ATI Stream and CUDA focuses on using the GPU’s stream processors in tandem with the CPU to enable the entire system to handle computing-intensive applications, and more specifically video transcoding applications.


ATI Stream technology overview

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(Courtesy of ATI)

Ryan first wrote about ATI’s new Stream technology back in November 2008, and since that time the basic premise behind the technology still stands. ATI Stream technology is based off a set of advanced hardware and software technologies that enable AMD graphics processors, working in concert with the system’s central processor, to accelerate many applications beyond just graphics. Stream technology enables hundreds of parallel Stream cores inside AMD graphics processors to accelerate general purpose applications. These capabilities will allow ATI Stream-enabled programs to operate with optimized performance or with new functionality.

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(Courtesy of ATI)

ATI Stream uses parallel computing architecture that will take advantage of thegraphics card’s stream processors to compute problems, applications or tasks that can be broken down into parallel, identical operations and run simultaneously on a single processor device. Stream computing also takes advantage of a SIMD methodology (single instruction, multiple data) whereas a CPU is a modified SISD methodology (single instruction, single data); modifications taking various parallelism techniques into account.

 

NVidia CUDA technology overview

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(Courtesy of NVidia)

NVidia CUDA is a general purpose parallel computing architecture that leverages the parallel compute engine in NVidia graphics processing units to solve many complex computational problems in a fraction of the time required on a CPU. It includes the CUDA Instruction Set Architecture and the parallel compute engine in the GPU. No GPU parallel computing architecture has been more in the spotlight than NVidia’s CUDA either. CUDA performs two major functions that consumers should be aware of – it helps reduce or match CPU usage by engaging the GPU’s stream processors and it can accelerate any computing process where CUDA is enabled.

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(Courtesy of NVidia)

NVidia claims to have sold more than 100 million CUDA-enabled GPUs to date which is probably accurate, and they are also being supported by thousands of software developers who NVidia says are already using the free CUDA software development tools to solve problems in a variety of professional and home applications.

Now that you have better insight into the history behind GPGPU technology as well as ATI and NVidia’s role in the technology’s development, let’s move on to the ATI Stream and CUDA-enabled video transcoding applications we will be using for our review today.


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