This project develops a dynamic ray scheduling algorithm that effectively manages both ray state and data accesses. Our algorithm can render datasets that are larger than aggregate system memory, which existing statically scheduled ray tracers cannot render. For smaller problems that fit in aggregate memory, but are larger than typical shared memory, our algorithm is competitive with the best static scheduling algorithm.
Ray tracing is an attractive technique for visualizing scientific data because it can produce high quality images that faithfully represent physically-based phenomena. Its embarrassingly parallel reputation makes it a natural candidate for visualizing large data sets on algoritm distributed memory clusters, especially for machines without specialized graphics hardware. Unfortunately, the traditional recursive ray tracing algorithm is exceptionally memory inefficient on large data, especially when using a shading model that generates incoherent secondary rays. As visualization moves from petascale to exascale, disk and memory efficiency will become increasingly important for performance which render traditional methods inadequate.
Paul A. Navrátil Manager, Scalable Vis Technologies Group, Visualization Scientist
Longhorn Supercomputer
