Volume Rendering Via Data-Parallel Primitives

Abstract

Supercomputing designs have recently evolved to include architectures beyond the standard CPU. In response, visualization software must be developed in a manner that obviates the need for porting all visualization algorithms to all architectures. Recent research results indicate that building visualization software on a foundation of dataparallel primitives can meet this goal, providing portability over many architectures, and doing it in a performant way. With this work, we introduce an unstructured data volume rendering algorithm which is composed entirely of data-parallel primitives. We compare the algorithm to community standards, and show that the performance we achieve is similar. That is, although our algorithm is hardware-agnostic, we demonstrate that our performance on GPUs is comparable to code that was written for and optimized for the GPU, and our performance on CPUs is comparable to code written for and optimized for the CPU. The main contribution of this work is in realizing the benefits of data-parallel primitives - portable performance, longevity, and programmability - for volume rendering. A secondary contribution is in providing further evidence of the merits of the data-parallel primitives approach itself.