A Vision for GPU-accelerated Parallel Computation on Geo-Spatial Datasets
Department of Computer Science, Georgia State University, USA
Sigspatial Newsletter Special issue on Big Spatial Data, 2014
@article{shekhar2014vision,
title={A Vision for GPU-accelerated Parallel Computation on Geo-Spatial Datasets},
author={Shekhar, Shashi and Zhou, Xun},
year={2014}
}
We summarize the need and present our vision for accelerating geo-spatial computations and analytics using a combination of shared and distributed memory parallel platforms, with general-purpose Graphics Processing Units (GPUs) with 100s to 1000s of processing cores in a single chip forming a key architecture to parallelize over. A GPU can yield one-to-two orders of magnitude speedups and will become increasingly more affordable and energy efficient due to mass marketing for gaming. We also survey the current landscape of representative geo-spatial problems and their parallel, GPU-based solutions.
February 24, 2015 by hgpu
