대용량 공간 데이터를 위한 병렬 처리 기법

Abstract

Graphical processing unit (GPU) contains many arithmetic logic units (ALUs). Because many ALUs can be exploited to process parallel processing, GPU provides efficient data processing. The spatial data require many geographic coordinates to represent the shape of them in a map. The coordinates are usually stored as geodetic longitude and latitude. To display a map in 2-dimensional Cartesian coordinate system, the geodetic longitude and latitude should be converted to the Universal Transverse Mercator (UTM) coordinate system. The conversion to the other coordinate system and the rendering process to represent the converted coordinates to screen use complex floating-point computations. In this paper, we propose a parallel processing technique that processes the conversion and the rendering using the GPU to improve the performance. Large spatial data is stored in the disk on files. To process the large amount of spatial data efficiently, we propose a technique that merges the spatial data files to a large file and access the file with the method of memory mapped file. We implement the proposed technique and perform the experiment with the 747,302,971 points of the TIGER/Line spatial data. The result of the experiment is that the conversion time for the coordinate systems with the GPU is 30.16 times faster than the CPU only method and the rendering time is 80.40 times faster than the CPU. The Graphical Processing Unit(GPU) is originally used for processing computer graphics. To assure rendering beautiful graphic effect, the GPU has a lot of arithmetic logic units (ALUs). The number of the GPU's ALU is larger than the CPU's. This makes the GPU operate floating point more rapid than the CPU. Compare with the GPU and the CPU computing speed, the GPU is superior to the CPU's computing speed. Therefore, the GPU can be used to process computations because of the GPU's overwhelming ability compared with the CPU. The GPU's cores are constructed in parallel. It is possible to be used to improve performance, which processes same calculation with many data. There are many researches about parallel processing using the GPU in many areas. In algorithm area, there are many researches about sorting algorithms in parallel (1,2,3). In multimedia area, researches are being preceded for image processing and coding moving