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.
Original language | English (US) |
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Pages (from-to) | 1023-1039 |
Number of pages | 17 |
Journal | International Journal of Geographical Information Science |
Volume | 27 |
Issue number | 5 |
DOIs | |
State | Published - May 2013 |
Keywords
- PySAL
- parallelization
- spatial analysis
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development
- Library and Information Sciences