Parallel optimal choropleth map classification in PySAL |
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Authors: | Sergio J Rey Luc Anselin Robert Pahle Xing Kang Philip Stephens |
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Institution: | 1. School of Geographical Sciences and Urban Planning, Arizona State University , Tempe , AZ , USA srey@asu.edu;3. School of Geographical Sciences and Urban Planning, Arizona State University , Tempe , AZ , USA |
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Abstract: | In this article, we report on our experiences with refactoring a spatial analysis library to support parallelization. Python Spatial Analysis Library (PySAL) is a library of spatial analytical functions written in the open-source language, Python. As part of a larger scale effort toward developing cyberinfrastructure of GIScience, we examine the particular case of choropleth map classification through alternative parallel implementations of the Fisher-Jenks optimal classification method using a multi-core, single desktop environment. The implementations rely on three different parallel Python libraries, PyOpenCL, Parallel Python, (PP) and Multiprocessing. Our results point to the dominance of the CPU-based Parallel Python and Multiprocessing implementations over the Graphical Processing Unit (GPU)-based PyOpenCL approach. |
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Keywords: | parallelization spatial analysis PySAL |
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