Volume rendering on scalable shared-memory MIMD architectures

Abstract

Volume rendering is a useful visualization technique for understanding the large amounts of data generated in a variety of scientific disciplines. Routine use of this technique is currently limited by its computational expense. We have designed a parallel volume rendering algorithm for MIMD architectures based on ray tracing and a novel task queue image partitioning technique. The combination of ray tracing and MIMD architectures allows us to employ algorithmic optimizations such as hierarchical opacity enumeration, early ray termination, and adaptive image sampling. The use of task queue image partitioning makes these optimizations efficient in a parallel framework. We have implemented our algorithm on the Stanford DASH Multiprocessor, a scalable shared-memory MIMD machine. Its single address-space and coherent caches provide programming ease and good performance for our algorithm. With only a few days of programming effort, we have obtained nearly linear speedups and near real-time frame update rates on a 48 processor machine. Since DASH is constructed from Silicon Graphics multiprocessors, our code runs on any Silicon Graphics workstation without modification.