<scp>pRPL</scp> 2.0: Improving the Parallel Raster Processing Library

Guan, Qingfeng; Zeng, Wen; Gong, Junfang; Yun, Shuo

doi:10.1111/tgis.12109

Cited by 25 publications

(8 citation statements)

References 47 publications

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“…The performance was much higher than other parallel CA models that only use multiple CPUs or CPU cores. For example, Guan et al (2014) implemented the same CA model using the parallel Raster Processing Library (pRPL), and conducted a series of experiments using the same datasets. On a computer cluster composed of 106 nodes, each equipped with four Opteron 16-core 2.1GHz CPUs and 256 GB of RAM, the pRPL-based CA model completed a 5-year simulation (without output) in 77.42 seconds using 1024 CPU cores (i.e., MPI processes).…”

Section: Results and Performance Assessmentsmentioning

confidence: 99%

A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures

Guan

Shi

Huang

et al. 2015

International Journal of Geographical Information Science

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As an important spatiotemporal simulation approach and an effective tool for developing and examining spatial optimization strategies (e.g., land allocation and planning), geospatial cellular automata (CA) models often require multiple data layers and consist of complicated algorithms in order to deal with the complex dynamic processes of interest and the intricate relationships and interactions between the processes and their driving factors. Also, massive amount of data may be used in CA simulations as high-resolution geospatial and non-spatial data are widely available. Thus, geospatial CA models can be both computationally intensive and data intensive, demanding extensive length of computing time and vast memory space. Based on a hybrid parallelism that combines processes with discrete memory and threads with global memory, we developed a parallel geospatial CA model for urban growth simulation over the heterogeneous computer architecture composed of multiple central processing units (CPUs) and graphics processing units (GPUs). Experiments with the datasets of California showed that the overall computing time for a 50-year simulation dropped from 13,647 seconds on a single CPU to 32 seconds using 64 GPU/CPU nodes. We conclude that the hybrid parallelism of geospatial CA over the emerging heterogeneous computer architectures provides scalable solutions to enabling complex simulations and optimizations with massive amount of data that were previously infeasible, sometimes impossible, using individual computing approaches.

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Section: Results and Performance Assessmentsmentioning

confidence: 99%

A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures

Guan

Shi

Huang

et al. 2015

International Journal of Geographical Information Science

Self Cite

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“…Additionally, cloud-based computing platforms, such as Google Earth Engine (GEE) for big earth observation data, have been increasingly used in geospatial studies and applications. To optimize the performance of a parallel algorithm for geospatial processing, analysis, or modeling when using such general-purpose frameworks, the spatial characteristics of the data and algorithm must be considered for the algorithmic design [15,16]. The four papers by Jo et al [3], Zhao et al [4], Kang et al [5], and Safanelli et al [6] focus on parallel computing and highlight the adaption of existing computing frameworks for geospatial data preprocessing, parallel algorithm design, simulation modeling, and data analysis.…”

Section: Big Data Computational Methodsmentioning

confidence: 99%

Introduction to Big Data Computing for Geospatial Applications

Tang

Huang

et al. 2020

IJGI

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The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.

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“…2014a; Guan et al 2014). The basic idea of these easyto-use parallel programming libraries is to encapsulate the parallel programming details of a specific parallel computing platform (Guan and Clarke 2010;Guan et al 2014) or even multiple platforms (Qin et al 2014a) so as to hide them from users. These efforts provide a promising step toward the easier design of parallel algorithms used for dealing with depressions and flats in DEMs, as well as of other raster-based geo-computation algorithms.…”

Section: Parallel Algorithmsmentioning

confidence: 99%

Review on algorithms of dealing with depressions in grid DEM

2019

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Depressions in grid digital elevation models (DEMs) need to be dealt with before the topographic attributes (such as specific catchment area) and terrain features (such as drainage networks) related to flow directions can be derived from DEMs in a hydrologically-correct manner. Many depression-processing algorithms, which adopt different strategies and take different information under consideration for determining correct flow directions in depressions, have been proposed. However, currently, there is still no one algorithm which can satisfactorily deal with depressions in grid DEMs under various application contexts. In this paper, we review existing depressionprocessing algorithms based on the adopted strategies (i.e. the DEM-revising strategy and the DEM-unchanging strategy). Algorithms with the DEM-revising strategy especially are discussed in detail according to their designs relating to the revision of DEM elevations, i.e. the smoothing filter, depression filling, depression breaching (or carving), using other qualified data, and applying different algorithms to depressions with different characteristics. Existing ways of improving the computation efficiency of depression-processing algorithms are also presented, i.e. serial algorithm optimization and parallel algorithms. Lastly, we discuss a possible design for an optimal depression-processing algorithm which may be developed in the future. ARTICLE HISTORY

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pRPL 2.0: Improving the Parallel Raster Processing Library

Cited by 25 publications

References 47 publications

A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures

A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures

Introduction to Big Data Computing for Geospatial Applications

Review on algorithms of dealing with depressions in grid DEM

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