Multi-GPU volume rendering using MapReduce

Jeff A. Stuart, Cheng Kai Chen, Kwan-Liu Ma, John D. Owens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations


In this paper we present a multi-GPU parallel volume rendering implemention built using the MapReduce programming model. We give implementation details of the library, including specific optimizations made for our rendering and compositing design. We analyze the theoretical peak performance and bottlenecks for all tasks required and show that our system significantly reduces computation as a bottleneck in the ray-casting phase. We demonstrate that our rendering speeds are adequate for interactive visualization (our system is capable of rendering a 10243 floating-point sampled volume in under one second using 8 GPUs), and that our system is capable of delivering both in-core and out-of-core visualizations. We argue that a multi-GPU MapReduce library is a good fit for parallel volume renderering because it is easy to program for, scales well, and eliminates the need to focus on I/O algorithms thus allowing the focus to be on visualization algorithms instead. We show that our system scales with respect to the size of the volume, and (given enough work) the number of GPUs.

Original languageEnglish (US)
Title of host publicationHPDC 2010 - Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Number of pages8
StatePublished - Dec 16 2010
Event19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010 - Chicago, IL, United States
Duration: Jun 21 2010Jun 25 2010


Other19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010
Country/TerritoryUnited States
CityChicago, IL


  • GPU
  • MapReduce
  • Volume rendering

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software


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