Tera-scale astronomical data analysis and visualization
Abstract
We present a high-performance, graphics processing unit (GPU) based framework for the efficient analysis and visualization of (nearly) terabyte (TB) sized 3D 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 1 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 that 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 radio telescopes.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- March 2013
- DOI:
- arXiv:
- arXiv:1211.4896
- Bibcode:
- 2013MNRAS.429.2442H
- Keywords:
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- methods: data analysis;
- techniques: miscellaneous;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Distributed;
- Parallel;
- and Cluster Computing;
- Computer Science - Graphics
- E-Print:
- 16 pages, 14 Figures, accepted for publication in Monthly Notices of the Royal Astronomical Society