Parallel mesh-partitioning algorithms for generating shape optimised partitions using evolutionary computing |
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| Authors: | A Rama Mohan Rao |
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| Affiliation: | 1. 1st Department of Surgery, Semmelweis University, Budapest, Hungary;2. 2nd Department of Pathology, Semmelweis University, Budapest, Hungary;3. 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary;4. Tumor Progression Research Group, Joint Research Organization of the Hungarian Academy of Sciences and Semmelweis University, Budapest, Hungary;1. Allergy Immunology and Respiratory Medicine, The Alfred Hospital and Department of Medicine Monash University, Melbourne, VIC, Australia;2. Departments of Medicine and Radiology University of California, La Jolla, San Diego, CA, USA;1. Barcelona Supercomputing Center (BSC), Workflows and Distributed Computing Group, Barcelona, 08034, Spain;2. Instituto Tecnológico Superior de Álamo Temapache, Xoyotitla, Veracruz, 92730, Mexico;3. Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC), Spain;1. Colorectal Unit, Department of General and Digestive Surgery, Hospital Arnau de Vilanova, Valencia, Spain;2. Digestive Motility Unit, Hospital Clínico Universitario, University of Valencia, Spain;3. Department of Surgery, University of Valencia, Spain |
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| Abstract: | In this paper, parallel mesh-partitioning algorithms are proposed for generating submeshes with optimal shape using evolutionary computing techniques. It is preferred to employ a formulation for mesh partitioning, which maintains constant number of design variables irrespective of the size of the mesh. Two distinct parallel computing models have been employed. The first model of parallel evolutionary algorithm uses the master–slave concept (single population model) and a new synchronous model is proposed to optimise the performance even on heterogeneous parallel hardware. Alternatively, a multiple population model is also developed which simulates it’s sequential counter part. The advantage of the second model is that it can fit in large size problems with large population even on moderate capacity parallel computing nodes. The performance of the evolutionary computing based mesh-partitioning algorithm is demonstrated first by solving several practical engineering problems and also several benchmark test problems available in the literature and comparing the results with the multilevel algorithms. Later the speedup of the parallel evolutionary algorithms on parallel hardware is evaluated by solving large scale practical engineering problems. |
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