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@InProceedings{ZellmannEtAl22,
  title     = {{Hybrid Image-/Data-Parallel Rendering Using Island Parallelism}},
  author    = {Stefan Zellmann and Ingo Wald and Joao Barbosa and 
              Serkan Demirci and Alper Sahistan and U{\^g}ur G{\"u}d{\"u}kbay},
  booktitle = {Proceedings of the IEEE 12th Symposium on Large Data Analysis and Visualization},
  pages     = {1--10},
  year      = {2022},
  month     = {6 December},
  editor    = {Johanna Beyer and Steffen Frey and Paul Navr{\' a}til},
  series    = {LDAV '22},
  publisher = {IEEE Computer Society},
  address   = {Washington, DC, USA},
  bib2html_dl_pdf = "http://www.cs.bilkent.edu.tr/~gudukbay/publications/papers/conf_papers/Zellmann_Et_Al_LDAV_2022.pdf",
  bib2html_pubtype = {Refereed Conference Papers},
  bib2html_rescat = {Computer Graphics},
  pdf = 	 {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9966396&tag=1},
  url = 	 {https://ieeexplore.ieee.org/document/9966396},
  ee        = {https://doi.org/10.1109/LDAV57265.2022.9966396},
  abstract  =  {In parallel ray tracing, techniques fall into one of two camps: image-parallel techniques 
                aim at increasing frame rate by replicating scene data across nodes and splitting the 
				rendering work across different ranks, and data-parallel techniques aim at increasing 
				the size of the model that can be rendered by splitting the model across multiple ranks, 
				but typically cannot scale much in frame rate. We propose and evaluate a hybrid approach 
				that combines the advantages of both by splitting a set of N x M ranks into M islands of 
				N ranks each and using data-parallel rendering within each island and image parallelism 
				across islands. We discuss the integration of this concept into four wildly different 
				parallel renderers and evaluate the efficacy of this approach based on multiple different data sets.}
}