Abstract: 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 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 through the petascale to the exascale, disk and memory efficiency will become increasingly important for performance, and traditional methods are inadequate. This paper presents 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 example, using 1024 cores of a supercomputing cluster, our unoptimized algorithm ray traces a 650GB dataset from an N-Body simulation with shadows and reflections, at about 1100 seconds per frame. 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.