A Distributed-Memory Parallel Approach for Volume Rendering with Shadows

Manish Mathai, Matthew C Larsen, Hank Childs

Room: 106

2023-10-22T22:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
Volume renderings of four data sets using shadows. From left to right, the data sets are Perlin Noise, Rayleigh-Taylor Instability, Richtmyer-Meshhkov Instability, and Rotating Stratified Turbulence.

We present a parallel, distributed-memory technique that enhances traditional ray-casting volume rendering of large data sets to highlight the depth and perception of interesting volumetric features. The technique introduces a lighting system that accounts for global shadows across distributed MPI nodes while using shared-memory parallelism within each node to compute shading information efficiently. The first stage of the approach involves estimating energy attenuation from a point light source through the global volume, using a reduced spatial resolution representation of the volume, with minimal global communication between nodes. It is then used in the second stage during volume rendering to shade sample points captured during ray-casting, generating a high-quality image. In this work, we study the technique's performance across varying spatial resolutions of the estimated light attenuation using synthetic and real-world volumetric data sets on distributed systems.