Video Transcript:

Scientific visualization is fundamental for researchers. Being able to see a visual representation of otherwise incomprehensible data is key to advancing the frontiers of human knowledge. One of the main ways researchers accurately visualize their data involves ray tracing. Ray tracing simulates photons of light as they bounce from a light source off an object and into our eyes, based on the laws of optics, providing a highly realistic image. As computers get faster, the datasets they generate become larger and more unwieldy, making visualization beyond the reach of some researchers. Developing better ways to visualize scientific data is critical if we want researchers to be able to keep up with the demand of their ever growing datasets. Working with researchers at the University of Oregon, the University of Utah, Intel and Paraview, we've developed a new framework called GraviT that makes it easier for scientists who have never visualized their data with ray tracing to begin doing so. Perhaps most importantly, GraviT makes it possible to visualize datasets so large they can't be explored with any other method. The geologists, astronomers, and fluid dynamicists who have used GraviT so far all say it makes it easier for them to make discoveries in their fields, which is really what we're after in the end. I'm Paul Navratil, manager of the Scalable Visualization Technologies group at the Texas Advanced Computing Center.