Release of CGLX Version 1.2.1

The most notable of these frameworks is the Cross-Platform Cluster Graphics Library (CGLX), which introduces a new approach to high-performance hardware accelerated visualization on ultra-high-resolution display systems. It provides a cluster management framework, a development API as well as a selected set of cluster-ready applications. Coinciding with the launch of the expanded HIPerSpace system, Calit2 today announced the official release of CGLX version 1.2.1, available for downloading at http://vis.ucsd.edu/~cglx . "There is no reason why you need to start from scratch every time you want to program an application for a visualization cluster," said CGLX developer Kai-Uwe Doerr, project scientist in Kuester's lab. "CGLX was developed to enable everybody to write real-time graphics applications for visualization clusters. The framework takes care of networking, event handling, access to hardware-accelerated rendering, and some other things. Users can focus on writing their applications as if they were writing them for a single desktop.”

With the emergence of OptIPortal technology, ultra-high resolution multi-tile display environments are no longer limited to a few select research facilities with highly specialized research teams supporting them. As a result, an intuitive yet powerful development framework is needed that supports fundamental research while enabling experts as well as novice users to utilize these systems. From a high-level view, CGLX creates a distributed, parallel graphics context and manages its state and events transparently – allowing the user to focus on content and context rather than how render nodes and displays are combined to show the final visual. CGLX enables OpenGL programs, developed for a single workstation, to be executed on a large-scale tiled visualization grid with minimal or no changes to the original code. The distributed nature of the framework supports and encourages the development of programs to generate visual analytics infrastructures, which enable researchers to collaboratively view, interrogate, correlate and manipulate data in real time with visual resolutions well beyond a single workstation. Key features of the framework include:
- Cross-platform, hardware-accelerated rendering (UNIX and Mac OSX support);
- Synchronized, multilayer OpenGL context support;
- Distributed event management; and
- Scalable multi-display support.

Applications using CGLX include a real-time viewer for gigapixel images and image collections, video playback, video streaming, and visualization of multi-dimensional models. The CGLX framework is already used by nearly all 90 megapixel-plus OptIPortals worldwide, and it is available for Linux (Fedora, RedHat, Suse), Rocks Cluster Systems (bundled in the hiperroll), and Mac OSX (leopard, tiger for ppc and Intel). CGLX is so flexible that it can even be scaled down to run on a commodity laptop. "With CGLX," explained Falko Kuester, "researchers can finally focus on solving demanding visualization and data analysis challenges on next-generation visual analytics cyberinfrastructure."

Two researchers in Kuester’s lab – Kevin Ponto and So Yamaoka – are developing visual analytics techniques to display gigapixel imagery at interactive (real-time) speeds on ultra-high resolution displays, notably the HIPerSpace wall. In a forthcoming publication, Ponto and Yamaoka demonstrate an application they developed on top of CGLX for use on the HIPerSpace wall. It uses OptIPuter networking to connect to remote storage clusters hosting target data sets, including the Spitzer Space Telescope Survey (for which each image of the inner Milky Way is 24,752 by 13,520 pixels), and NASA’s Blue Marble visualizations of the Earth at monthly intervals (86.4 million x 43.2 million pixels).


“These ultra-scale visualization techniques load data adaptively and progressively from network attached storage, requiring only a small local memory footprint on each display node, while avoiding data replication,” explained graduate-student Ponto. “All data is effectively loaded on demand in accordance with the locally available display resources.” Added fellow Computer Science and Engineering Ph.D. student Yamaoka: “A render node driving a single four-megapixel display, for example, will only fetch the data needed to fill that display at any given point in time. If the viewing position is updated, the needed data is again fetched, on demand.”

HIPerSpace: By the Numbers

Number of tiles: 70
(fully supported in networked configuration)
Display resolution: 35,840 x 8,000 pixels, 286,720,000 pixels total
Number of display nodes: 18
Number of streaming nodes: 6
Control and development nodes: 3
Combined HIPerSpace-HIPerWall connectivity: 491,520,000 pixels in distributed configuration

Hardware
18 Dell XPS710 w/ NVIDIA Quadro FX5600s, 72 Dell 3007WFP-HC, 30” Displays
2 Dell 2004WFP, 24” Displays
6 Shuttle SG31G2
2 24-port SMC switches with 10Gb uplink

Operating System
ROCKS/Linux

Middleware
CGLX

Links

GRAVITY Lab at UC San Diego
HIPerSpace
CGLX
NASA Spitzer Space Telescope Survey
NASA Blue Marble
Intel
NVIDIA
Dell