A Heterogeneous HPC Platform for Ill-Structured Spatial
Join Processing
SessionDoctoral Showcase Session 3
Presenter
Event Type
Doctoral Showcase
Algorithms
Applications
Architectures
Heterogeneous Systems
TimeTuesday, November 14th4:24pm -
4:42pm
Location210-212
DescriptionGiven two layers of large polygonal datasets, detecting
those pairs of cross-layer polygons which satisfy a join
predicate, such as intersection, is one of the most
computationally intensive primitive operations in the
spatial domain applications. There are alternative
solutions for this ill-structured problem in literature.
However, none of them is designed to take advantage of
heterogeneous clusters equipped with GPU accelerators to
process big spatial data efficiently.
In this research, we propose a distributed heterogeneous HPC platform for spatial join processing based on two-step filter and refinement approach. This work includes two main parts. First, we introduce a set of novel GPU-suited data structures and algorithms. Proof of correctness and analysis is also provided for each algorithm. Second, by applying these new techniques, we propose several GPU-based spatial join systems for various operations such as ST_intersect and polygon overlay to improve the performance of current state of the art systems.
In this research, we propose a distributed heterogeneous HPC platform for spatial join processing based on two-step filter and refinement approach. This work includes two main parts. First, we introduce a set of novel GPU-suited data structures and algorithms. Proof of correctness and analysis is also provided for each algorithm. Second, by applying these new techniques, we propose several GPU-based spatial join systems for various operations such as ST_intersect and polygon overlay to improve the performance of current state of the art systems.
Presenter
