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

• Published in: Computers, environment and urban systems Vol. 61; pp. 187 - 197
• Main Authors: Tang, Wenwu, Feng, Wenpeng
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.01.2017; Elsevier Science Ltd
• Subjects: Algorithms; Big Data; Big spatial data; Circuits; Cloud computing; Couples; Coupling; Graphics processing units; Lidar; Map projection; Parallel computing; Projection; Spatial analysis; Spatial data; Cloud computing; Parallel computing; Big spatial data; Map projection; Graphics processing units
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2014.01.001

•We developed a cloud-based parallel map projection framework of big spatial data.•Capabilities of cloud computing and GPU-enabled high-performance computing were coupled.•Cloud computing allows for on-demand deployment of the parallel map projection framework.•Map projection of vector-based big spatial data is substantially accelerated by GPUs. The objective of this article is to present a framework that couples cloud and high-performance computing for the parallel map projection of vector-based big spatial data. The past few years have witnessed a tremendous growth of a variety of high-volume spatial data—i.e., big spatial data. Map projection is often needed, for example, when we apply these big spatial data into large-scale spatial analysis and modeling approaches that require a common coordinate system. However, due to the size of these data and algorithmic complexity of map projections, the transformation of big spatial data between alternative projections represents a pressing computational challenge. Recent advancement in cloud computing and high-performance computing offers a potential means of addressing this computational challenge. The parallel map projection framework presented in this study is based on a layered architecture that couples capabilities of cloud computing and high-performance computing accelerated by Graphics Processing Units. We use large LiDAR data as an example of vector-based big spatial data to investigate the utility of the parallel map projection framework. As experimental results reveal, the framework provides considerable acceleration for re-projecting vector-based big spatial data. Coupling high-performance and cloud computing, which complement to each other, is a suggested solution for the efficient processing and analysis of big spatial data.