Help For the Rest of Us
I like an eye-popping benchmark as well as the next guy. But at the end of the day, I’m a user. I use computers to do useful things. And on days when I have to give back the Tom’s Hardware Lear jet, and all the ski bunnies go back to their warrens, I have a modest computer with modest components and not much budget to spare for $500 upgrades. I need technology that’s going to help me do what I want more efficiently, whether it’s play games, edit video, or help model genetic sequences.
Some applications are linear in nature and merely want to crank as quickly as possible on a single processing thread until the cows come home. Others are built to leverage parallelism. Everything from Unreal Engine 3 to Adobe Premiere has shown us the benefits of CPU-based multi-threading, but what if 4 or 8 or even 16 threads was just a beginning?
This is the promise behind Nvidia’s CUDA computing architecture, which, according to the company’s definition, can run thousands of threads simultaneously.
We've written about CUDA in the past, so hopefully you’re no stranger to the technology (if you did miss our coverage, check out Nvidia's CUDA: The End of the CPU?) For better or worse, though, most CUDA coverage in the press has focused on high-end hardware, even though the supporting logic has been present in Nvidia GPUs since the dawn of the GeForce 8. When you consider the huge enterprise dollars wrapped up in the high-performance computing (HPC) and professional graphics workstation markets—targeted by Nvidia’s Tesla and Quadro lines, respectively—no wonder this is where so much of Nvidia’s marketing attention has been.
But in 2009, we finally see a change. CUDA has come to the masses. There's a huge install base of compatible desktop graphics cards, and the mainstream applications able to exploit that built-in CUDA support are hitting one after the other.
From Nothing To Now
The first consumer-friendly CUDA app was Folding@Home, a university distributed computing project out of Stanford in which each user can crunch a chunk of raw data about protein behavior so as to better understand (and hopefully cure) several of humanity’s worst diseases. The application transitioned to CUDA compatibility in the second half of 2008. Very shortly afterward came Badaboom, the video transcoder from Elemental Technologies that, according to Elemental, can transcode up to 18 times faster than a CPU-only implementation.
Then came a whole slew of media applications for CUDA: Adobe Creative Suite 4, TMPGEnc 4.0 XPress, CyberLink PowerDirector 7, MotionDSP vReveal, Loilo LoiLoScope, Nero Move it, and more. Mirror’s Edge looks to be the first AAA game title to fully leverage CUDA-based PhysX technology for increasing visual complexity, allegedly by 10x to 20x. Expect to see more titles emerge in this vein—a lot more. While AMD and its ATI Stream technology have been mired in setbacks, Nvidia has been hyping its finished and proven CUDA to everyone who will listen...and developers now seem to be taking the message to heart.
That’s all well and good, but proof of CUDA’s incendiary capabilities has largely been proven on high-end GPUs. I’m on a tight budget. Friends are getting mowed down around me by lay-offs and wage-cuts like bubonic plague victims. You bet, I’d love to drop ten or twelve Benjamins on a 3-way graphics overhaul, but the reality is that, like many of you, I’ve only got one or two C-notes to spare. On a good day. So the question all of us who can’t afford the graphics equivalent of a five-star menage-a-troi should be asking is, “Does CUDA mean anything to me when all I can afford is a budget-friendly card for my existing system?”
Let’s find out. Today, we'll be looking at some of the most promising titles and measuring the speed-up garnered from a pair of mid-range GPUs.
