A fast pipelined parallel ray casting algorithm using advanced space leaping method

Hyung Jun Kim, Yong Je Woo, Yong Won Kwon, So Hyun Ryu, Chang Sung Jeong

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper we present a very fast pipelined parallel ray casting algorithm for volume rendering. Our algorithm is based on an extended space leaping method which minimizes the traversal of data and image space by using run-length encoding and line drawing algorithms. We propose a more advanced space leaping method which allows the efficient implementation of parallel forward projection by merging the run-lengths for the line drawing. We shall show that the whole algorithm is sharply speed up by reducing the time taken to project the run-lengths onto the image screen, and by exploiting the pipelined parallelism in our space leaping method. Also, we shall show the experimental result of the parallel ray casting algorithm implemented on our Computational Grid portal environment.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsVictor Malyshkin
PublisherSpringer Verlag
Pages244-252
Number of pages9
ISBN (Print)3540406735
DOIs
Publication statusPublished - 2003 Jan 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2763
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Grid
  • Grid Portal
  • Parallel ray casting
  • Space leaping
  • Volume rendering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, H. J., Woo, Y. J., Kwon, Y. W., Ryu, S. H., & Jeong, C. S. (2003). A fast pipelined parallel ray casting algorithm using advanced space leaping method. In V. Malyshkin (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 244-252). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2763). Springer Verlag. https://doi.org/10.1007/978-3-540-45145-7_22