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Towards personal high-performance geospatial computing (HPC-G): perspectives and a case study

Author:

Jianting ZhangAuthors Info & Claims

HPDGIS '10: Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems

Pages 3 - 10

https://doi.org/10.1145/1869692.1869694

Published: 02 November 2010 Publication History

Abstract

Cluster computing, Cloud computing and GPU computing play overlapping and complementary roles in parallel processing of geospatial data within the general HPC framework. The fast increasing hardware capacities of modern personal computers equipped with chip multiprocessor CPUs and massively parallel GPUs have made high performance computing of large-scale geospatial data in a personal computing environment possible. We discuss the framework of Personal HPC-G and compare it with traditional Cluster computing and the newly emerging Cloud computing. We consider Personal HPC-G possesses many favorable features: low initial and operational costs, good support for data management and excellent support for both numeric modeling and interactive visualization. A case study on developing a parallel spatial statistics module for visual explorations on top of Personal HPC-G is subsequently presented.

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Cited By

Zhang G(2022)PyCLKDE: A big data‐enabled high‐performance computational framework for species habitat suitability modeling and mappingTransactions in GIS10.1111/tgis.1290126:4(1754-1774)Online publication date: 10-Feb-2022
https://doi.org/10.1111/tgis.12901
Zhang GZhu ALiu JGuo SZhu Y(2021)PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mappingTransactions in GIS10.1111/tgis.1273025:3(1396-1418)Online publication date: 16-Feb-2021
https://doi.org/10.1111/tgis.12730
Stojanovic NStojanovic D(2019)Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACCISPRS International Journal of Geo-Information10.3390/ijgi80903868:9(386)Online publication date: 3-Sep-2019
https://doi.org/10.3390/ijgi8090386
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Published In

cover image ACM Conferences

HPDGIS '10: Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems

November 2010

47 pages

ISBN:9781450304320

DOI:10.1145/1869692

General Chairs:
Shaowen Wang
University of Illinois at Urbana-Champaign
,
Nancy Wilkins-Diehr
University of California at San Diego
,
Program Chairs:
Xuan Shi
Georgia Institute of Technology
,
Ranga Raju Vatsavai
Oak Ridge National Laboratory
,
Jianting Zhang
City College of the City University of New York

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2010

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GIS '10

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SIGSPATIAL

GIS '10: 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2, 2010

California, San Jose

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Cited By

Zhang G(2022)PyCLKDE: A big data‐enabled high‐performance computational framework for species habitat suitability modeling and mappingTransactions in GIS10.1111/tgis.1290126:4(1754-1774)Online publication date: 10-Feb-2022
https://doi.org/10.1111/tgis.12901
Zhang GZhu ALiu JGuo SZhu Y(2021)PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mappingTransactions in GIS10.1111/tgis.1273025:3(1396-1418)Online publication date: 16-Feb-2021
https://doi.org/10.1111/tgis.12730
Stojanovic NStojanovic D(2019)Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACCISPRS International Journal of Geo-Information10.3390/ijgi80903868:9(386)Online publication date: 3-Sep-2019
https://doi.org/10.3390/ijgi8090386
Kang XLiu JDong CXu S(2018)Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big DataISPRS International Journal of Geo-Information10.3390/ijgi70702737:7(273)Online publication date: 11-Jul-2018
https://doi.org/10.3390/ijgi7070273
Song SDeng LGong JLuo H(2018)Gaia Scheduler: A Kubernetes-Based Scheduler Framework2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)10.1109/BDCloud.2018.00048(252-259)Online publication date: Dec-2018
https://doi.org/10.1109/BDCloud.2018.00048
Qin CZhan LZhu AZhou C(2018)A strategy for raster-based geocomputation under different parallel computing platformsInternational Journal of Geographical Information Science10.1080/13658816.2014.91130028:11(2127-2144)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1080/13658816.2014.911300
Tang WFeng WDeng JJia MZuo H(2017)Parallel Computing for Geocomputational ModelingGeoComputational Analysis and Modeling of Regional Systems10.1007/978-3-319-59511-5_4(37-54)Online publication date: 31-Jul-2017
https://doi.org/10.1007/978-3-319-59511-5_4
Sun ZZhang HLiu ZXu CWang L(2016)Migrating GIS Big Data Computing from Hadoop to Spark: An Exemplary Study Using Twitter2016 IEEE 9th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD.2016.0054(351-358)Online publication date: Jun-2016
https://doi.org/10.1109/CLOUD.2016.0054
Zhang JYou SGruenwald L(2015)Large-scale spatial data processing on GPUs and GPU-accelerated clustersSIGSPATIAL Special10.1145/2766196.27662016:3(27-34)Online publication date: 22-Apr-2015
https://dl.acm.org/doi/10.1145/2766196.2766201
Li XHuang TLu DNiu C(2014)Accelerating experimental high-order spatial statistics calculations using GPUsComputers & Geosciences10.1016/j.cageo.2014.05.01270(128-137)Online publication date: Sep-2014
https://doi.org/10.1016/j.cageo.2014.05.012
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