Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units

Tang, Wenwu; Feng, Wenpeng

doi:10.1016/j.compenvurbsys.2014.01.001

Cited by 44 publications

(23 citation statements)

References 27 publications

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“…datasets for the estimation of mangrove biomass and carbon at the global level. In other words, the use of these fine-resolution geospatial data for the global-level mangrove biomass and carbon estimation represents a big data-driven challenge (see [17][18][19]). Therefore, to address this big data-driven challenge, the objective of this study is to conduct an estimation of global mangrove biomass and carbon by using a GIS-based spatial analysis approach integrated with parallel computing.…”

Section: Datamentioning

confidence: 99%

“…As advancement in cyberinfrastructure [28,29], high-performance computing resources such as computing clusters (with hundreds or thousands of CPUs or higher) are increasingly available. Parallel computing approaches allow for the partitioning of a large spatial analysis task that is challenging or infeasible for single CPUs into a set of smaller sub-tasks [18,30,31]. These smaller tasks, which are computationally efficient or feasible, can be deployed to the high-performance computing clusters and executed concurrently by the collection of multiple computing elements (CPUs) on these clusters.…”

Section: Parallel Computing For Accelerated Geospatial Analysis Of Mamentioning

confidence: 99%

“…The study in this article aims to estimate biomass and carbon in global-level mangrove forests to demonstrate an approach Even higher storage and computing capacities are required to process and analyze these datasets for the estimation of mangrove biomass and carbon at the global level. In other words, the use of these fine-resolution geospatial data for the global-level mangrove biomass and carbon estimation represents a big data-driven challenge (see [17][18][19]). …”

Section: Introductionmentioning

confidence: 99%

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Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation

et al. 2018

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Abstract:The objective of this study is to estimate the biomass and carbon of global-level mangroves as a special type of wetland. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove biomass and carbon stock. However, this estimation at a global level has been insufficiently investigated because the spatial scale of interest is large and most existing studies are based on physically challenging fieldwork surveys that are limited to local scales. Over the past few decades, high-resolution geospatial data related to mangroves have been increasingly collected and processed using remote sensing and Geographic Information Systems (GIS) technologies. While these geospatial data create potential for the estimation of mangrove biomass and carbon, the processing and analysis of these data represent a big data-driven challenge. In this study, we present a spatially explicit approach that integrates GIS-based geospatial analysis and high-performance parallel computing for the estimation of mangrove biomass and carbon at the global level. This integrated approach provides support for enabling and accelerating the global-level estimation of mangrove biomass and carbon from existing high-resolution geospatial data. With this integrated approach, the total area, biomass (including above-and below-ground), and associated carbon stock of global mangroves are estimated as 130,420 km 2 , 1.908 Pg, and 0.725 Pg C for the year of 2000. The averaged aboveground biomass density of global mangroves is estimated as 146.3 Mg ha −1 . Our analysis results demonstrate that this integrated geospatial analysis approach is efficacious for the computationally challenging estimation of global mangrove metrics based on high-resolution data. This global-level estimation and associated results are of great assistance for promoting our understanding of complex geospatial dynamics in mangrove forests.

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Section: Datamentioning

confidence: 99%

Section: Parallel Computing For Accelerated Geospatial Analysis Of Mamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation

et al. 2018

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“…CUDA has been utilized to support map projections as well. Tang and Feng [25] report an effort of designing a CUDA-based parallel algorithm for projecting vector data. When designing and implementing a CUDA-based map reprojection method for raster datasets, three issues of GPU parallel processing should be addressed.…”

Section: Literature Reviewmentioning

confidence: 99%

A Lightweight CUDA-Based Parallel Map Reprojection Method for Raster Datasets of Continental to Global Extent

Finn

Castano

2017

IJGI

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Geospatial transformations in the form of reprojection calculations for large datasets can be computationally intensive; as such, finding better, less expensive ways of achieving these computations is desired. In this paper, we report our efforts in developing a Compute Unified Device Architecture (CUDA)-based parallel algorithm to perform map reprojections for raster datasets on personal computers using Graphics Processing Units (GPUs). This algorithm has two unique features: a) an output-space-based parallel processing strategy to handle transformations more rigorously, and b) a chunk-based data decomposition method for projected space in conjunction with an on-the-fly data retrieval mechanism to avoid memory overflow. To demonstrate the performance of our CUDA-based map reprojection approaches, we have conducted tests between this method and the traditional serial version using the Central Processing Unit (CPU). The results show that speedup ratios range from 10 times to 100 times in all test scenarios. The lessons learned from the tests are summarized.

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“…It is usually stored in a distributed manner on multiple computers. Several distributed High Performance Computing (HPC) models like Graphics Processing Units (GPUs) (Owens et al, 2006;Tang and Feng, 2017) and MapReduce (Gao et al, 2014;Giachetta, 2015) are available to handle big data. Among them, MapReduce is most widely accepted models because it works on the concept of parallel computing.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Conventional Hadoop With Proposed Cloud Enabled Hadoop Framework for Spatial Big Data Processing

Tripathi

Agrawal

Gupta

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The emergence of new tools and technologies to gather the information generate the problem of processing spatial big data. The solution of this problem requires new research, techniques, innovation and development. Spatial big data is categorized by the five V’s: volume, velocity, veracity, variety and value. Hadoop is a most widely used framework which address these problems. But it requires high performance computing resources to store and process such huge data. The emergence of cloud computing has provided, on demand, elastic, scalable and payment based computing resources to users to develop their own computing environment. The main objective of this paper is to develop a cloud enabled hadoop framework which combines cloud technology and high computing resources with the conventional hadoop framework to support the spatial big data solutions. The paper also compares the conventional hadoop framework and proposed cloud enabled hadoop framework. It is observed that the propose cloud enabled hadoop framework is much efficient to spatial big data processing than the current available solutions.</p>

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Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units

Cited by 44 publications

References 27 publications

Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation

Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation

A Lightweight CUDA-Based Parallel Map Reprojection Method for Raster Datasets of Continental to Global Extent

A Comparative Analysis of Conventional Hadoop With Proposed Cloud Enabled Hadoop Framework for Spatial Big Data Processing

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