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JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

Matthias Labschütz, Stefan Bruckner, M. Eduard Gröller, Markus Hadwiger, Peter Rautek

JOURNAL ARTICLE: IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 1025–1034, 2016. DOI: 10.1109/TVCG.2015.2467331

Abstract

Abstract—Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for com putation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Published

IEEE Transactions on Visualization and Computer Graphics

  • Volume: 22
  • Number: 1
  • Pages: 1025–1034
  • Event: IEEE SciVis 2015
  • Location: Chicago, USA
  • Date: January 2016
  • DOI: 10.1109/TVCG.2015.2467331

Documents and Links

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BibTeX

@ARTICLE{Labschuetz-2016-JJC,
  author = {Matthias Labsch{\"u}tz and Stefan Bruckner and M. Eduard Gr{\"o}ller
	and Markus Hadwiger and Peter Rautek},
  title = {JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2016},
  volume = {22},
  pages = {1025--1034},
  number = {1},
  month = jan,
  abstract = {Abstract—Sparse volume data structures enable the efficient representation
	of large but sparse volumes in GPU memory for com putation and visualization.
	However, the choice of a specific data structure for a given data
	set depends on several factors, such as the memory budget, the sparsity
	of the data, and data access patterns. In general, there is no single
	optimal sparse data structure, but a set of several candidates with
	individual strengths and drawbacks. One solution to this problem
	are hybrid data structures which locally adapt themselves to the
	sparsity. However, they typically suffer from increased traversal
	overhead which limits their utility in many applications. This paper
	uses just-in-time compilation to overcome these problems. By combining
	multiple sparse data structures and reducing traversal overhead we
	leverage their individual advantages. We demonstrate that hybrid
	data structures adapt well to a large range of data sets. They are
	especially superior to other sparse data structures for data sets
	that locally vary in sparsity. Possible optimization criteria are
	memory, performance and a combination thereof. Through just-in-time
	(JIT) compilation, JiTTree reduces the traversal overhead of the
	resulting optimal data structure. As a result, our hybrid volume
	data structure enables efficient computations on the GPU, while being
	superior in terms of memory usage when compared to non-hybrid data
	structures.},
  doi = {10.1109/TVCG.2015.2467331},
  event = {IEEE SciVis 2015},
  keywords = {data transformation and representation, GPUs and multi-core architectures,
	volume rendering},
  location = {Chicago, USA},
}






 Last Modified: Stefan Bruckner, 2017-10-13