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Spatial Data Analytics on Homogeneous Multi-Core Parallel Architectures

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Encyclopedia of GIS

Synonyms

concurrent processing; vector spatial analysis

Main Text

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyberinfrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in...

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References

  • Aggarwal A, Chazelle B, Guibas L, O’Dunlaing C, Yap C (1985) Parallel computational geometry. In: Proceedings of the IEEE symposium on foundations of computer science, Portland, pp 468–477

    MATH  Google Scholar 

  • Akman W, Franklin WR, Kankanhalli C, Narayanaswami C (1989) Geometric computing and uniform grid technique. Comput Aided Design 21(7):410–420

    Article  Google Scholar 

  • Amdahl GM (1967) Validity of the single-processor approach to achieving large scale computing capabilities. In: AFIPS conference proceedings, Washington, DC, pp 483–485

    Google Scholar 

  • Anselin L, Rey SJ (2012) Spatial econometrics in an age of CyberGIScience. Int J Geogr Inf Sci 26(12):2211–2226

    Article  Google Scholar 

  • Arjomandi E (1975) A study of parallelism in graph theory. PhD thesis, Department of Computer Science, University of Toronto

    Google Scholar 

  • Armstrong MP (2000) Geography and computational science. Ann Assoc Am Geogr 90(1):146–156

    Article  MathSciNet  Google Scholar 

  • Armstrong MP, Densham PJ (1992) Domain decomposition for parallel processing of spatial problems. Comput Environ Urban Syst 16(6):497–513

    Article  Google Scholar 

  • Armstrong MP, Pavlik CE, Marciano R (1993) Parallel processing of spatial statistics. Comput Geosci 20(2):91–104

    Article  Google Scholar 

  • Armstrong MP, Cowles MK, Wang S (2005) Using a computational grid for geographic information analysis: a reconnaissance. Profess Geogr 57(2005):365–375

    Article  Google Scholar 

  • Asanovic K, Bodik R, Catanzaro BC, Gebis JJ, Husbands P, Keutzer K, Patterson DA, Plishker WL, Shalf J, Williams SW, Yelick KA (2006) The landscape of parallel computing research: a view from berkley. Technical report UCB/EECS-2006-183, Eletrical Engineering and Computer Sciences University of California at Berkeley

    Google Scholar 

  • Atkins DE, National Science Foundation (US) Blue-Ribbon Advisory Panel on Cyberinfrastructure (2003) Revolutionizing science and engineering through cyberinfrastructure: report of the national science foundation blue-ribbon advisory panel on cyberinfrastructure. National Science Foundation

    Google Scholar 

  • Bell GC, Gray J (1997) The revolution yet to happen. In: Denning PJ, Metcalf RM (eds) Beyond calculation: the next fifty years of computing. Copernicus, New York

    Google Scholar 

  • Blelloch GE, Little JJ (1988) Parallel solutions to geometric problems on the scan model of computation, Memo, vol 952. MIT Artificial Intelligence Laboratory, Cambridge

    Google Scholar 

  • Clematis A, Mineter M, Marciano R (2003) High performance computing with geographical data. Parallel Comput 29:1275–1279

    Article  Google Scholar 

  • Grama A, Gupta A, Karypis G, Kumar V (2003) Introduction to parallel computing. Addison-Wesley, Harlow

    MATH  Google Scholar 

  • Griffith DA (1990) Supercomputing and spatial statistics: a reconnaissance. Profess Geogr 42(4):481–492

    Article  Google Scholar 

  • Gustafson JL (1988) Reevaluating Amdahl’s law. Commun ACM 31(5):532–533

    Article  Google Scholar 

  • Healey R, Dowers S, Gittings B, Mineter M (eds) (1998) Parallel processing algorithms for GIS. Taylor and Francis, Bristol

    Google Scholar 

  • Hirschberg DS (1976) Parallel algorithms for the transitive closure and the connected component problems. In: Proceedings of the 8th ACM symposium on theory of computing, Hershey, pp 55–57

    Google Scholar 

  • Hoel EG, Samet H (2003) Data-parallel polygonization. Parallel Comput 29:1381–1401

    Article  MathSciNet  Google Scholar 

  • James T, Rego C, Glover F (2009) A cooperative parallel tabu search algorithm for the quadratic assignment problem. Eur J Oper Res 195(3):810–826

    Article  MATH  Google Scholar 

  • Laura J, Rey SJ (2014) Improved parallel optimal choropleth map classification. In: Shi X et al (ed) Modern accelerator technologies for geographic information science. Springer Science+Business Media, New York, pp 197–212

    Google Scholar 

  • Laura J, Li W, Rey SJ, Anselin L (2015) Parallelization of a regionalization heuristic in distributed computing platforms parallelization of a regionalization heuristic in distributed computing platforms: a case study of parallelp-compact-regions problem. Int J Geogr Inf Sci 29(4):536–555

    Article  Google Scholar 

  • Rey S, Anselin L, Pahle R, Kang X, Stephens P (2013) Parallel optimal choropleth map classification in PySAL. Int J Geogr Inf Sci 27(5):1023–1039

    Article  Google Scholar 

  • Rokos D-KlD, Armstrong MP (1996) Using Linda to compute spatial autocorrelation in parallel. Comput Geosci 22(4):425–432

    Article  Google Scholar 

  • VanDerwerken DN, Schmidler SC (2013) Parallel Markov Chain monte carlo. Biometrika. ArXiv e-prints 1312.7479

    Google Scholar 

  • Vaughan J (1991) A parallel implementation of the Douglas-Peucker line simplification algorithm. Softw Pract Exp 21(3):331–336

    Article  MathSciNet  Google Scholar 

  • Wang S, Armstrong MP (2003) A quadtree approach to domain decomposition for spatial interpolation in Grid computing environments. Parallel Comput 29(10):1481–1504

    Article  Google Scholar 

  • Wang S, Armstrong MP (2005) A theory of the spatial computational domain. In: Proceedings of the 8th international conference on geocomputation, Ann Arbor

    Google Scholar 

  • Waugh TC, Hopkins S (1992) An algorithm for polygon overlay using cooperative parallel processing. Int J Geogr Inf Syst 6(6):457–467

    Article  Google Scholar 

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Authors and Affiliations

  1. GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, 210076, 85721, Tucson, AZ, USA

    Jason Laura & Sergio J. Rey

Authors
  1. Jason Laura
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  2. Sergio J. Rey
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Correspondence to Jason Laura .

Editor information

Editors and Affiliations

  1. Minneapolis, Minnesota, USA

    Shashi Shekhar

  2. Rutgers Business School, Newark, New Jersey, USA

    Hui Xiong

  3. The University of Iowa Tippie College of Business, Iowa City, Iowa, USA

    Xun Zhou

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Laura, J., Rey, S.J. (2016). Spatial Data Analytics on Homogeneous Multi-Core Parallel Architectures. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-23519-6_1569-1

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  • DOI: https://doi.org/10.1007/978-3-319-23519-6_1569-1

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  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-23519-6

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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