Jianting Zhang City College of New York jzhang@cs.ccny.cuny.edu Indexing Large-Scale Raster Geospatial Data Using Massively Parallel GPGPU Computing Jianting Zhang City College of New York jzhang@cs.ccny.cuny.edu Simin You CUNY Graduate Center syou@gc.cuny.edu Le Gruenwald University of Oklahoma ggruenwald@ou.edu Advances in remote sensing technologies and environmental modeling have and will generated huge amount of large-scale raster geospatial data. For example, the GOES-R geostationary operational environmental satellite to be lunched in 2015 has a spatial resolution of 2 kilomters and temporal resolution of 5 minutes. This translates to nearly a quarter of a billion raster cells in each of the 288 global coverages daily for a single band.

GPU-Based Parallel Processing of Large-Scale Raster Data From field measurements To multi-dimensional arrays To tree indices Traditionally database indexing is considered expensive… Can we borrow some computing power from gamers that own tens of millions of graphic cards? In spatial database applications, we construct indices to speed up query processing. Traditionally database indexing is considered expensive and requires considerable computing resources. On the other hand, thanks to millions of game lovers, current commodity GPU devices have provided tremedious computing power at very inexpensive prices. Many of the current generation GPU devices support general purpose computing. The basic idea in this research is to investigate the feasibility of using GPU to index large-scale raster geospatial data for interactive visual explorations. GPGPU technologies are for both gamers and scientists!

Data Structure, Parallel Algorithm and Results Nvidia Quadro FX3700 Card 112 core (500M HZ) 512M Device Memory 23X speedup compared to a single 2G HZ Intel E5405 CPU core We have designed a Cache Conscious quadtree data structure and a set of algorithms to construct tree indices on GPUs. The construction algorithms utilize a set of pyramids but do not use pointers which are suitable for GPUs. Experiments using a three years old Nvidia Quadro Fx3700 card with 112 cores have shown 23 times speed up when compared to an Intel CPU core, even though the clock rate of the CPU core is four times faster than the GPU cores. While the index construction process shown in this slide might look complicated, we hope to convince you that planting a tree out of silicon is far more easier than growing a tree in your backyard. So please do come to our poster and we will show you how. Come to our poster and we will show you how to grow trees out of matrices on GPUs!