Parallel Visualization Algorithms: Performance and Architectural Implications

Jaswinder Pal Singh, Anoop Gupta, Marc Levoy

Research output: Contribution to specialist publicationArticle

84 Scopus citations


Recently, a new class of scalable, shared-address-space multiprocessors has emerged. Like message-passing machines, these multiprocessors have a distributed interconnection network and physically distributed main memory. However, they provide hardware support for efficient implicit communication through a shared address space, and they automatically exploit temporal locality by caching both local and remote data in a processor's hardware cache. In this article, we show that these architectural characteristics make it much easier to obtain very good speedups on the best known visualization algorithms. Simple and natural parallelizations work very well, the sequential implementations do not have to be fundamentally restructured, and the high degree of temporal locality obviates the need for explicit data distribution and communication management. We demonstrate our claims through parallel versions of three state-of-the-art algorithms: A recent hierarchical radiosity algorithm by Hanrahan et al. (1991), a parallelized ray-casting volume renderer by Levoy (1992), and an optimized ray-tracer by Spach and Pulleyblank (1992). We also discuss a new shear-warp volume rendering algorithm that provides the first demonstration of interactive frame rates for a 256/spl times/256/spl times/256 voxel data set on a general-purpose multiprocessor.

Original languageEnglish (US)
Number of pages11
Specialist publicationComputer
StatePublished - Jul 1994
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science


Dive into the research topics of 'Parallel Visualization Algorithms: Performance and Architectural Implications'. Together they form a unique fingerprint.

Cite this