We present a high-performance, graphics processing unit (GPU)-based framework for the efficient analysis and visualization of (nearly) terabyte (TB)-sized 3-dimensional images. Using a cluster of 96 GPUs, we demonstrate for a 0.5 TB image: (1) volume
rendering using an arbitrary transfer function at 7--10 frames per second; (2) computation of basic global image statistics such as the mean intensity and standard deviation in 1.7 s; (3) evaluation of the image histogram in 4 s; and (4) evaluation of the global image median intensity in just 45 s. Our measured results correspond to a raw computational throughput approaching one teravoxel per second, and are 10--100 times faster than the best possible performance with traditional single-node, multi-core CPU implementations. A scalability analysis shows the framework will scale well to images sized 1 TB and beyond. Other parallel data analysis algorithms can be added to the framework with relative ease, and accordingly, we present our framework as a possible solution to the image analysis and visualization requirements of next-generation telescopes, including the forthcoming Square Kilometre Array pathfinder radiotelescopes.
We demonstrate how interactive, three-dimensional (3-d) scientific visualizations can be efficiently interchanged between a variety of mediums. Through the use of an appropriate interchange format, and a unified interaction interface, we minimize the
effort to produce visualizations appropriate for undertaking knowledge discovery at the astronomers desktop, as part of conference presentations, in digital publications or as Web content. We use examples from cosmological visualization to address some of the issues of interchange, and to describe our approach to adapting S2PLOT desktop visualizations to the Web. Supporting demonstrations are available at http://astronomy.swin.edu.au/s2plot/interchange/
