A vision for GPU-accelerated parallel computation on geo-spatial datasets
Sushil K. Prasad, Michael McDermott, Satish K. Puri, Dhara Shah, Danial Aghajarian, Shashi Shekhar, Xun S. Zhou · SIGSPATIAL Special · 2015
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.