Hands-on with Deep Viewer, Intel's Potential Killer-App for Nehalem

Posted 08/21/2008 at 2:30pm | by Norman Chan

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During a private briefing with Intel at IDF yesterday to talk about Nehalem, we were given a demo of some cool software in development that makes good use of the multi-threaded cores of the new CPU. Francois Piednoel, the Senior Performance Analyst (ie. benchmarking guru) at Intel describes Deep Viewer as a "science project" of sorts. It's an image sorting application that they acquired from an independent software developer that reminds us of Microsoft Live Labs' Seadragon technology (which is used in the recently released Photosynth online app). We're talking about near-infinite scaling of visual data (in this case photos and videos) being processed in real-time on your display.

In the first part of the demo, we were introduced to a Core i7-powered system running in tri-channel memory mode with a fancy-looking 30" display. The monitor was actually a touchscreen (enabled by that exposed silicon around the bezel) and one of the things Francois did first was move some objects around with his finger. 

When it started up, the Deep Viewer app showed a small calendar grid next to a world map. Pinching the calendar -- like gesturing on an iPhone -- expanded the frame. And as the grid grew, we could see images populating the space within each calendar day. Francois kept zooming in, and more images revealed themselves on the fly. 

This was actually 200GB worth of images, stored off of a 500GB hard drive. Over 224 terapixels of data was accessible in this demo, being streamed into view and processed in real-time. The scaling was incredibly fast, and we were told the app didn't store any cache or pre-process files. The images that popped up to full resolution as we zoomed in weren't just jpegs, either. RAW photos, bitmaps, and other image formats all work with Deep Viewer -- legacy file and codec support is one of the reasons a general-purpose CPU is optimal for this kind of app. 

In addition to images, many of the thumbnails were actually videos, too! 640x480 (non-HD) clips shot with a power-and-shoot camera were split up into indexed scenes and played alongside the high-res jpegs. One really cool feature is the app's ability to run facial recognition algorithms on videos when you zoom in on them. We focused on a scene of some passengers walking off of a train, and a red circle highlighted each face to pick up details and find matches in other photos in the database.

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