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A Vision for GPU-accelerated Parallel Computation on Geo-Spatial Datasets

Sushil K. Prasad, Michael McDermott, Satish Puri, Dhara Shah, Danial Aghajarian, Shashi Shekhar, Xun Zhou
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}

}

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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.
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