@ARTICLE {Labschuetz-2016-JJC,
author = "Matthias Labsch{\"u}tz and Stefan Bruckner and Meister 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",
number = "1",
pages = "1025--1034",
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  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.",
pdf = "pdfs/Labschuetz-2016-JJC.pdf",
images = "images/Labschuetz-2016-JJC.jpg",
thumbnails = "images/Labschuetz-2016-JJC.png",
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"
}