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The Australian SKA Pathfinder (ASKAP) will be producing 2.2 terabyte HIspectral-line cubes for each 8 hours of observation by 2013. Global views ofspectral data cubes are vital for the detection of instrumentation errors, theidentification of data artefacts and noise characteristics, and the discoveryof strange phenomena, unexpected relations, or unknown patterns. We havepreviously presented the first framework that can render ASKAP-sized cubes atinteractive frame rates. The framework provides the user with a real-timeinteractive volume rendering by combining shared and distributed memoryarchitectures, distributed CPUs and graphics processing units (GPUs), using theray-casting algorithm. In this paper we present two main extensions of thisframework which are: using a multi-panel display system to provide a highresolution rendering output, and the ability to integrate automated dataanalysis tools into the visualization output and to interact with its output inplace.

Distributed GPU Volume Rendering of ASKAP Spectral Data Cubes