Distributed GPU Volume Rendering of ASKAP Spectral Data Cubes

TL;DR

This paper presents an extended distributed GPU volume rendering framework for large ASKAP spectral data cubes, enabling high-resolution visualization and integrated data analysis for astronomical research.

Contribution

It introduces multi-panel display support and integrated data analysis tools into the existing real-time volume rendering framework for ASKAP data.

Findings

Achieved high-resolution rendering of large spectral data cubes.

Enabled real-time interaction with integrated data analysis.

Demonstrated improved visualization capabilities for astronomical data.

Abstract

The Australian SKA Pathfinder (ASKAP) will be producing 2.2 terabyte HI spectral-line cubes for each 8 hours of observation by 2013. Global views of spectral data cubes are vital for the detection of instrumentation errors, the identification of data artefacts and noise characteristics, and the discovery of strange phenomena, unexpected relations, or unknown patterns. We have previously presented the first framework that can render ASKAP-sized cubes at interactive frame rates. The framework provides the user with a real-time interactive volume rendering by combining shared and distributed memory architectures, distributed CPUs and graphics processing units (GPUs), using the ray-casting algorithm. In this paper we present two main extensions of this framework which are: using a multi-panel display system to provide a high resolution rendering output, and the ability to integrate automated data analysis tools into the visualization output and to interact with its output in place.