A vision for GPU-accelerated parallel computation on geo-spatial datasets

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.

[1]  Vikram Jandhyala,et al.  A Variant of Parallel Plane Sweep Algorithm for Multicore Systems , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  Paul S. Heckbert,et al.  Graphics gems IV , 1994 .

[3]  Jiming Liu,et al.  Speeding up K-Means Algorithm by GPUs , 2010, 2010 10th IEEE International Conference on Computer and Information Technology.

[4]  J. Ord,et al.  Local Spatial Autocorrelation Statistics: Distributional Issues and an Application , 2010 .

[5]  Sushil K. Prasad,et al.  Lessons Learnt from the Development of GIS Application on Azure Cloud Platform , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[6]  Shashi Shekhar,et al.  GPGPU-accelerated interesting interval discovery and other computations on GeoSpatial datasets: a summary of results , 2013, BigSpatial '13.

[7]  Roy H. Campbell,et al.  A Parallel Implementation of K-Means Clustering on GPUs , 2008, PDPTA.

[8]  Mark McKenney,et al.  Geospatial overlay computation on the GPU , 2011, GIS.

[9]  Kunle Olukotun,et al.  Accelerating CUDA graph algorithms at maximum warp , 2011, PPoPP '11.

[10]  Zbigniew J. Czech,et al.  Introduction to Parallel Computing , 2017 .

[11]  Suprio Ray,et al.  Speeding up Spatial Database Query Execution using GPUs , 2012, ICCS.

[12]  Shashi Shekhar,et al.  Discovering interesting sub-paths in spatiotemporal datasets: a summary of results , 2011, GIS.

[13]  Masana Murase,et al.  Robust and efficient polygon overlay on parallel stream processors , 2013, SIGSPATIAL/GIS.

[14]  Prabhas Chongstitvatana,et al.  Spatial Join with R-Tree on Graphics Processing Units , 2013 .

[15]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[16]  Fusheng Wang,et al.  Haggis: turbocharge a MapReduce based spatial data warehousing system with GPU engine , 2014, BigSpatial '14.

[17]  Xi He,et al.  MapReduce Algorithms for GIS Polygonal Overlay Processing , 2013, 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum.

[18]  Xi He,et al.  A System for GIS Polygonal Overlay Computation on Linux Cluster - An Experience and Performance Report , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[19]  Sergio J. Rey,et al.  PySAL: A Python Library of Spatial Analytical Methods , 2010 .

[20]  Sartaj Sahni,et al.  GPU Matrix Multiplication , 2013 .

[21]  David G. Tarboton.  High Performance Hydrologic Terrain Analysis and CyberGIS , 2014 .

[22]  Sushil K. Prasad,et al.  Acceleration of Bilateral Filtering Algorithm for Manycore and Multicore Architectures , 2012, 2012 41st International Conference on Parallel Processing.

[23]  Joel H. Saltz,et al.  Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems , 2012, Proc. VLDB Endow..

[24]  Michael Mikolajczak,et al.  Designing And Building Parallel Programs: Concepts And Tools For Parallel Software Engineering , 1997, IEEE Concurrency.

[25]  I. Noble,et al.  A Model of the Responses of Ecotones to Climate Change. , 1993, Ecological applications : a publication of the Ecological Society of America.

[26]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[27]  Ahmed Eldawy,et al.  CG_Hadoop: computational geometry in MapReduce , 2013, SIGSPATIAL/GIS.

[28]  Ramesh C. Agarwal,et al.  A three-dimensional approach to parallel matrix multiplication , 1995, IBM J. Res. Dev..

[29]  David Sun,et al.  UNIFORM GRIDS: A TECHNIQUE FOR INTERSECTION DETECTION ON SERIAL AND PARALLEL MACHINES , 2008 .

[30]  Mark McKenney,et al.  A parallel plane sweep algorithm for multi-core systems , 2009, GIS.

[31]  Ryan Johnson,et al.  A parallel spatial data analysis infrastructure for the cloud , 2013, SIGSPATIAL/GIS.

[32]  Qing He,et al.  Parallel K-Means Clustering Based on MapReduce , 2009, CloudCom.

[33]  Divyakant Agrawal,et al.  Hardware acceleration for spatial selections and joins , 2003, SIGMOD '03.

[34]  Xi He,et al.  Design and implementation of a parallel priority queue on many-core architectures , 2012, 2012 19th International Conference on High Performance Computing.

[35]  Jianting Zhang,et al.  Speeding up large-scale point-in-polygon test based spatial join on GPUs , 2012, BigSpatial '12.

[36]  Sushil K. Prasad,et al.  Cloud Computing for Fundamental Spatial Operations on Polygonal GIS Data , 2012 .