Spatial Indexing of Large-Scale Geo-Referenced Point Data on GPGPUs Using Parallel Primitives
نویسنده
چکیده
Modern positioning and locating technologies, e.g., GPS, have generated huge amounts of geo-referenced point data that are crucial to understand environmental and social-economic phenomena. Unfortunately, traditional disk-resident databases are inefficient in handling large-scale point data. In this study, we propose to utilize the massive data parallel processing power of General Purpose computing on Graphics Processing Units (GPGPUs) technologies to index large-scale geo-referenced point data by using parallel primitives for efficiency, simplicity and portability. We have developed a CSPT-P (Constrained Space Partitioning tree for Point data) tree indexing structure that is suitable for parallel construction. Experiment results using a New York City (NYC) taxi trip dataset with nearly 170 million taxi pickup locations have demonstrated a 23X speedup on an Nvidia Quadro 6000 device over a serial CPU implementation on an Intel XEON E5405 processor.
منابع مشابه
Data Parallel Quadtree Indexing and Spatial Query Processing of Complex Polygon Data on GPUs
Fast growing computing power on commodity parallel hardware makes it both an opportunity and a challenge to use modern hardware for large-scale data management. While GPU (Graphics Processing Unit) computing is conceptually an excellent match for spatial data management which is both data and computing intensive, the complexity of multi-dimensional spatial indexing and query processing techniqu...
متن کاملSimplifying High-Performance Geospatial Computing on GPGPUs Using Parallel Primitives: A Case Study of Quadtree Constructions on Large-Scale Geospatial Rasters
The increasingly available Graphics Processing Units (GPU) hardware resources and the emerging General Purpose computing on GPU (GPGPU) technologies provide an alternative and complementary solution to existing cluster based high-performance geospatial computing. However, the complexities of the unique GPGPU hardware architectures and the steep learning curve of GPGPU programming have imposed s...
متن کاملHigh-performance quadtree constructions on large-scale geospatial rasters using GPGPU parallel primitives
The increasingly available Graphics Processing Units (GPU) hardware and the emerging General Purpose computing on GPU (GPGPU) technologies provide an attractive solution to high-performance geospatial computing. In this study, we have proposed a parallel primitive based approach to quadtree construction by transforming a multidimensional geospatial computing problem into chaining a set of gener...
متن کاملHigh-Performance Spatial Join Processing on GPGPUs with Applications to Large-Scale Taxi Trip Data
Spatially joining GPS recorded locations with infrastructure data, such as points of interests, road network, land cover and different types of zones, and assigning a point with its nearest polyline or polygon is a prerequisite for trip related analysis, which is becoming increasingly important in ubiquitous computing. However, existing spatial databases and GIS are incapable of handling large-...
متن کاملSpeeding Up Geospatial Polygon Rasterization on GPGPUs
This study targets at speeding up polygon rasterization in large-scale geospatial datasets by utilizing massively parallel General Purpose Graphics Processing Units (GPGPU) computing for efficient spatial indexing and analysis based on a dynamically integrated vector-raster data model. As the first step, we have designed and implemented a parallelization schema for moderately large polygons usi...
متن کامل