Geocomputation's future at the extremes: high performance computing and nanoclients
Parallel Computing - Special issue: High performance computing with geographical data
Exploiting the Power of GPUs for Asymmetric Cryptography
CHES '08 Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems
Fundamenta Informaticae - Membrane Computing
International Journal of Geographical Information Science
A work-efficient GPU algorithm for level set segmentation
Proceedings of the Conference on High Performance Graphics
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
Speedups between ×70 and ×120 for a generic local search (memetic) algorithm on a single GPGPU chip
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Optimization strategies in different CUDA architectures using llCoMP
Microprocessors & Microsystems
A general-purpose geosimulation infrastructure for spatial decision support
Transactions on Computational Science VI
A comparison of evolutionary algorithms for automatic calibration of constrained cellular automata
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part I
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In recent years, geosimulation models are becoming increasingly sophisticated and applied to real-world problems covering large geographical areas. As a result, they often require extended computing times. However, in spite of the improved availability of parallel computing facilities, the applications in the field of urban and regional dynamics modelling are almost always based on sequential algorithms. This paper makes a contribution towards a wider use of some high performance computing techniques, namely those based on General-Purpose computing on Graphics Processing Units (GPGPU), in the geosimulation applications. In particular, the relevant details of a parallel version of a typical Cellular Automata approach for simulating land-use dynamics are presented. Also, some computational results obtained on two typical GPU devices are discussed.