Volume rendering with data parallel visualization frameworks for emerging high performance computing architectures

Hendrik A. Schroots, Kwan Liu Ma

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

5 Scopus citations

Abstract

Future exascale computing is demanding more and more parallelism from current software if peak computation rates are to be realized. However, exploiting this additional parallelism is not trivial. One approach is to identify finer grained parallelism using data parallel primitives (DPP). Visualization frameworks such as Dax and VTK-m are being developed using DPP for this purpose. Our work presents an exploratory study of how volume rendering maps to current and future super computing architectures. We implement a ray casting and cell projection volume renderer in Dax using DPP and compare their performance on three different hardware architectures. Despite the portability provided by these frameworks, we observe that additional architecture specific modifications are necessary to achieve acceptable performance on some architectures.

Original languageEnglish (US)
Title of host publicationSIGGRAPH Asia 2015 Visualization in High Performance Computing, SA 2015
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450339292
DOIs
Publication statusPublished - Nov 2 2015
EventSIGGRAPH Asia, SA 2015 - Kobe, Japan
Duration: Nov 2 2015Nov 6 2015

Other

OtherSIGGRAPH Asia, SA 2015
CountryJapan
CityKobe
Period11/2/1511/6/15

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Keywords

  • Data parallel primitives
  • Exascale computing
  • Many integrated core
  • Volume rendering

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Schroots, H. A., & Ma, K. L. (2015). Volume rendering with data parallel visualization frameworks for emerging high performance computing architectures. In SIGGRAPH Asia 2015 Visualization in High Performance Computing, SA 2015 [3] Association for Computing Machinery, Inc. https://doi.org/10.1145/2818517.2818546