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

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: October 2015
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Volume: 22
• Number: 1
• Note: Published in January 2016
• Location: Chicago, IL, USA
• Lecturer: Matthias Labschütz
• ISSN: 1077-2626
• Event: IEEE SciVis 2015
• Conference date: 25. October 2015 – 30. October 2015
• Pages: 1025 – 1034

Abstract

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation 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.

Additional Files and Images

Weblinks

• http://dx.doi.org/10.1109/TVCG.2015.2467331

BibTeX

@article{Labschuetz_Matthias_2015_JIT,
  title =      "JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data
               Structure",
  author =     "Matthias Labsch\"{u}tz and Stefan Bruckner and Eduard
               Gr\"{o}ller and Markus Hadwiger and Peter Rautek",
  year =       "2015",
  abstract =   "Sparse volume data structures enable the efficient
               representation of large but sparse volumes in GPU memory for
               computation 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.",
  month =      oct,
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "22",
  number =     "1",
  note =       "Published in January 2016",
  issn =       "1077-2626",
  pages =      "1025--1034",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2015/Labschuetz_Matthias_2015_JIT/",
}