If you need lots of compute power, you’re either already using GPU accelerators or taking a close look at them. And if you’re serious about tapping into the full processing power of the GPU, you need to take a look at the newest PCIe switch offering from One Stop Systems.

Dubbed the ioMillennia, it’s a 3U, PCIe Gen3 switch that can handle sixteen full-size GPU cards. We glimpsed this behemoth when walking the floor at GTC 2013 and were attracted like moths to the 42.3 billion candle-power Luxor Sky Beam. (Or, more accurately, like me to a donut display case.)

One Stop Systems VP of Sales Jim Ison walked us through the box, discussing both technical specs and customer usage. It’s quite an achievement, packing 16 hot and power-hungry GPUs into a cramped 3U space. And it does take a little bit of power to run 16 high-performance GPUs – about 6,000 watts provided by three 3,000-watt hot-swappable power supplies. (One is a redundant backup.)

Let’s say you’re at a gathering – maybe a cocktail party or a crowded club – and some buff athlete shows up on crutches. He immediately becomes the center of attention as he recounts the story of his injury: “Dude, it was gnarly… total sick shred. Now I’m waitin’ on the MRI results. The docs think I might never shred again, but I’m stoked to get back out there.”

Is there any way that you, a computer geek, can get the conversational focus back where it belongs –  on you? Sure you can; just say this:  “Really? You’re waiting? They can do MRIs in real time now, and not just an image, but a whole movie. Like better than YouTube, yo… here’s how it works…”

At last week’s GPU Technology Conference in San Jose, researchers from Cal Berkeley and the Max Planck Institute showed how MRI images can now be processed so quickly and accurately that they can be shown as full-motion, real-time videos instead of static, printed output. First, a little background:

We know that all of the objects in the picture below are chairs, right? But show this picture to a computer and see what happens. Getting computers to recognize generic objects is a hugely difficult task that’s complicated by variations of the same object (club chairs vs. office chairs vs. folding chairs) and by other objects in the computer’s field of view that can confuse the machine.

Researchers at iRobot have been working hard on object recognition. iRobot is best known as the company behind the Roomba vacuum cleaner, but they’ve been doing a lot more than just vacc’ing up the dander – these guys are doing serious research and development.

At last week’s GPU Technology Conference, the team from iRobot revealed that they’ve developed the first real-time generic object recognition algorithm. It’s based on the Deformable Part Model (DPM), which is based on the idea that objects are made of parts, and the way those parts are configured in relation to one another is what defines a person from a chair or a car from a boat.

Accurately modeling and rendering the ocean is highly intense from a computational perspective – but it’s considerably harder to accurately portray an artificial simulation of the human face, according to NVIDIA CEO Jen-Hsun Huang in his opening keynote address at the GTC 2013 conference in San Jose.

Human faces contain a subtlety that’s incredibly difficult to capture. Consider the wide range of skin textures, pore sizes, bumps, humps, colors, and other features that all have to interact in a completely natural manner in order for the illusion to hold. And that’s before we even think about modeling beards, goatees, and the like.

Anyone attempting to build a believable human facsimile also has to beware of the “uncanny valley.” This is a school of thought that says human copies which are almost – but not quite – perfectly human can cause big-time revulsion in us real humans.

(I would submit a picture of Carrot Top as proof positive that this is true. I’m sure you can come up with your own examples of real people who have gone over the uncanny cliff into the valley of revulsion. Spend a couple of hours on it today; it’s important, and sort of scientific too.)

NVIDIA CEO Jen-Hsun Huang’s GTC 2013 keynote was a typical whirlwind tour (with real wind, but that’s a different article) through all the various GPU-related worlds that NVIDIA is touching these days. These addresses are usually chock-full of demonstrations showing where we are in terms of state-of-the-art graphics, scientific and technical computing, entertainment, and now: finding dresses.

In this demonstration, Jen Hsun leafed through the latest edition of InStyle magazine. While the models are svelte (or starved), the magazine definitely isn’t, weighing in with 594 pages of ads. A dress from one of those ads was chosen, its picture was taken, and it was sent off for image matching. What came back was a set of likely matches that the image-matching tool found via eBay. (This can be seen in the semi-blurry picture taken from my third-row perch.)