Tera-scale Astronomical Data Analysis and Visualization

A. H. Hassan, C. J. Fluke, D. G. Barnes, V. A. Kilborn
Centre for Astrophysics and Supercomputing, Swinburne University of Technology, POBox 218, Hawthorn, Australia, 3122
arXiv:1211.4896 [astro-ph.IM] (20 Nov 2012)


   author={Hassan}, A.~H. and {Fluke}, C.~J. and {Barnes}, D.~G. and {Kilborn}, V.~A.},

   title={"{Tera-scale Astronomical Data Analysis and Visualization}"},

   journal={ArXiv e-prints},




   keywords={Astrophysics – Instrumentation and Methods for Astrophysics, Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Graphics},




   adsnote={Provided by the SAO/NASA Astrophysics Data System}


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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.
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