Distributed frameworks and parallel algorithms for processing large-scale geographic data
Parallel Computing - Special issue: High performance computing with geographical data
Parallel implementation of geometric shortest path algorithms
Parallel Computing - Special issue: High performance computing with geographical data
IPython: A System for Interactive Scientific Computing
Computing in Science and Engineering
Matplotlib: A 2D Graphics Environment
Computing in Science and Engineering
Sensitivity analysis of spatial models
International Journal of Geographical Information Science
Short communication: GRID computing approach for multireservoir operating rules with uncertainty
Environmental Modelling & Software
Introduction to distributed geographic information processing research
International Journal of Geographical Information Science - Distributed Geographic Information Processing Research
Environmental Modelling & Software
The gputools package enables GPU computing in R
Bioinformatics
The Art of Multiprocessor Programming
The Art of Multiprocessor Programming
ggplot2: Elegant Graphics for Data Analysis
ggplot2: Elegant Graphics for Data Analysis
Modelling uncertainty of a land management map derived from a time series of satellite images
International Journal of Remote Sensing
Parallel cellular automata for large-scale urban simulation using load-balancing techniques
International Journal of Geographical Information Science
Parallel Programming: for Multicore and Cluster Systems
Parallel Programming: for Multicore and Cluster Systems
Environmental Modelling & Software
Assessment of GPU computational enhancement to a 2D flood model
Environmental Modelling & Software
Environmental Modelling & Software
Efficient data IO for a Parallel Global Cloud Resolving Model
Environmental Modelling & Software
Computers and Electronics in Agriculture
A high performance GPU implementation of Surface Energy Balance System (SEBS) based on CUDA-C
Environmental Modelling & Software
Environmental Modelling & Software
A layered approach to parallel computing for spatially distributed hydrological modeling
Environmental Modelling & Software
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Integrated spatio-temporal assessment and modelling of complex social-ecological systems is required to address global environmental challenges. However, the computational demands of this modelling are unlikely to be met by traditional Geographic Information System (GIS) tools anytime soon. I evaluated the potential of a range of high-performance computing (HPC) hardware and software tools to overcome these computational barriers. Performance advantages were quantified using a synthetic model. Four tests were compared, using: a) an Arc Macro Language (AML) GIS script on a single central processing unit (CPU); b) Python/NumPy on 1-256 CPU cores; c) Python/NumPy on 1-64 graphics processing units (GPUs) with high-level PyCUDA abstraction (GPUArray); and d) Python/NumPy on 1-64 GPUs with low-level PyCUDA abstraction (ElementwiseKernel). The GIS implementation effectively took 15.5 weeks to run. Python/NumPy on a single CPU core led to a speed-up of 59x compared to the GIS. On a single GPU, speed-ups of 1473x were achieved using GPUArray and 4881x using ElementwiseKernel. Parallel processing led to further performance enhancements. At best, the ElementwiseKernel module in parallel over 64 GPUs achieved a speed-up of 63,643x. Open source tools such as Python applied across a spectrum of HPC resources offer transformational and accessible performance improvements for integrated assessment and modelling. By reducing the computational barrier, HPC can lead to a step change in modelling sophistication, including the better representation of uncertainty, and perhaps even new modelling paradigms. However, migration to new hardware and software environments also has significant costs. Costs and benefits of HPC are discussed and code tools are provided to help others migrate to HPC and transform our ability to address global challenges through integrated assessment and modelling.