Autostereoscopic display of large-scale scientific visualization

Modern computational science poses two challenges for scientific visualization: managing the size of resulting datasets and extracting maximum knowledge from them. While our team attacks the first problem by implementing parallel visualization algorithms on supercomputing architectures at vast scale, we are experimenting with autostereoscopic display technology to aid scientists in the second challenge. We are building a visualization framework connecting parallel visualization algorithms running on one of the world's most powerful supercomputers with high-quality autostereo display systems. This paper is a case study of the development of an end-to-end solution that couples scalable volume rendering on thousands of supercomputer cores to the scientists' interaction with autostereo volume rendering at their desktops and larger display spaces. We discuss modifications to our volume rendering algorithm to produce perspective stereo images, their transport from supercomputer to display system, and the scientists' 3D interactions. A lightweight display client software architecture supports a variety of monoscopic and autostereoscopic display technologies through a flexible configuration framework. This case study provides a foundation that future research can build upon in order to examine how autostereo immersion in scientific data can improve understanding and perhaps enable new discoveries.