nVidia held a scaled-down GPU (graphics processing unit) Conference in San Jose Sept. 29-Oct 2, 2009. The conference was much smaller than the previous year’s nVision blowout, which was like a block party for more than 5,000 people in downtown San Jose, occupying all available venues with LAN parties for gamers, classes for developers, celebrities for the masses, and pitch rooms from entrepreneurs. This year, the scene was quieter, with 1,500 focused attendees, many of them developers, sitting in three types of forums, which occupied most of the second floor of the Fairmont Hotel.

The three forums were, essentially, plenary sessions on general subjects, smaller academic seminars on theoretical topics, and “emerging company” gatherings, where small firms currently using nVidia technology could show their wares. The “why” of having these three types of activities together, while not immediately apparent, becomes clearer when you realize that nVidia is trying to communicate a fairly esoteric message through all of this showmanship: GPUs will change how computing is done, enabling entirely new ways of doing things.

The academics possess proofs of concept; they were able to show what will be. The emerging companies are running real applications; they demonstrated what is. And the plenary sessions — in addition to being a forum for nVidia to tout its good works and how it is contributing to this industry ferment — presented the broader architectural picture and highlighted industry developments with key nVidia partners.

The primary thesis behind GPU computing is that, with a lot of small processors doing little jobs in parallel, GPUs can get certain kinds of work done faster. That’s why nVidia called its early products, designed for gamers, graphics accelerators. GPU computing is evolving toward applying the same parallel concept to computing tasks of a more general nature. Some of these tasks — such as underground mapping for oil and gas exploration — render visible output, and so may be confused with pure graphics acceleration. Others — such as Monte Carlo simulations, used by Bloomberg to price millions of collateralized debt obligations (CDOs) — have no particular graphic output.

The CDO pricing model illustrates well how GPU computing changes how work gets done. Bloomberg was able to drop its requirement for running this complex application from 1,000 servers down to 48 when it paired nVidia Tesla GPUs with the eight-core x86 CPUs in each system. In addition to handing an ever more complex financial model, the hybrid GPU-CPU architecture enabled the company to run the model in a couple of hours rather than overnight. By accelerating this important task, Bloomberg was able to change its business model. Rather than next day, it could offer on-demand results, for which it could charge a premium.

More importantly, some of this acceleration is allowing execution in real time of tasks that were formerly iterative. Until now, you had to run, wait, look at results, change things, run again, and repeat until done. Now, results are in real time or nearly so. A demo done by nVidia CEO Jen-Hsun Huang of physical re-rendering of high-quality ray tracing took about eight seconds to resolve into a new view when parameters were changed. With this type of responsiveness, an artist rather than a technologist can run the tool.

A great example of this was shown by Richard Kerris, CTO of Industrial Light and Magic (ILM), in one of the plenary sessions. ILM, which is George Lucas’s vehicle for various properties such as Lucas Film Animation, Lucas Licensing, Lucas Online, Lucas Arts, and Skywalker Sound, is adopting hybrid computing with GPUs to simulate complex animated scenes. Years ago, when the firm did the simulation of the killer wave for Perfect Storm, the one that finally toppled George Clooney’s fishing boat, it took so long to render that they were able to run it only once. The output, made up of millions of fluid particles, looked, in its unvarnished form, like a tiny tsunami curling its way across a pan of Elmer’s Glue. “That’s the one we had to use,” said Kerris of the single instance.

To create the maelstrom across which the ships fought in the Pirates of the Caribbean II, ILM engineers needed 20 hours to render a single frame. To run smoothly, video needs at least 30 frames per second, and this scene went on for several minutes. At that rate one minute of render takes 36,000 hours or more than four years. Of course, ILM achieved some time savings by running the render across multiple machines in the company’s render farm.

Kerris cited a more recent example in which ILM used hybrid computing with GPUs to reduce the time per frame it took to render the fire scene from Harry Potter from 13 hours to 10 seconds. This quantum leap in computing power will change the way animated movies are made. Artists can see the results of their tweaks soon after making them and have a chance to go back and make the work better.

Some of the emerging companies showed how they are using GPU computing to layer objects onto video, create visual training materials, make more realistic online games, and drop the user into a 3D virtual world by way of a captured and manipulated camera image. In real time. Such fast execution has brought real time tools to market so that artists, analysts, and explorers can use them, not just software gurus and engineers.

Although nVidia gets some bragging rights for creating this conference and highlighting these ideas, there is nothing to say that other firms’ GPU computing effort won’t contribute to this revolution. AMD also has graphics chips being used to accelerate computing tasks. Even Intel has an effort underway to bring out its own discrete graphics chips, which may someday play a role in GPU computing.

© 2009 Endpoint Technologies Associates, Inc. All rights reserved.