IBM's new visualization technology

IBM last week announced IBM Deep Computing Visualization (DCV), a technology that combines IBM xSeries IntelliStation workstations and middleware to support high-performance visualization.

The idea behind DCV is to adapt visualization to the way life scientists work today.

For example, many past high-performance visualization efforts focused on immersive environments such as 3D caves or walls of tiled monitors. "These were great when you could bring everyone together," says Becky Austen, director of Deep Computing Marketing. But she notes that today, many life science companies and research organizations rely on collaboration between dispersed groups, each with different areas of expertise.

To address this challenge, DCV technology offers secure remote access to visualization and collaboration tools.

At the same time, IBM is addressing the shift away from Unix visualization systems. Similar to computational biology's shift away from proprietary systems, the DCV approach uses commodity components (e.g., central and graphical processing units) and open-source graphics applications to support high-performance visualization.

Specifically, the DCV provides a middleware infrastructure to support and enhance the graphics functions of OpenGL software applications running on IntelliStation workstations, which run Linux.

According to market research firm IDC, the goal of the DCV is to leverage the price/performance advantage of commodity graphics components and InfiniBand or Gigabit Ethernet adapters, without sacrificing the needs of high-end users.

To support visualization in a distributed research environment, the DCV offers two visualization modes for high-end images. First, there is the Scalable Visual Networking (SVN) mode, which lets a researcher increase the screen resolution and image size when an image is displayed. The second mode, called Remote Visual Networking (RVN), allows remote use of a visualization application. In general, the SVN supports larger and higher-resolution images, such as those that might be found in an immersive environment; RVN lets researchers collaborate over low-bandwidth networks.

One consequence of this dual-mode operation is that it might help organizations build visualization into complex workflows. Today, most visualization projects are designed to run on a specific system, and the results are optimized for the display hardware that the primary researchers would use. By being able to quickly provide access to a graphic image regardless of the display hardware, visualization could be incorporated into a normal workflow.

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