Design and implementation of a massively parallel version of DIRECT. This paper describes several massively parallel implementations for a global search algorithm DIRECT. Two parallel schemes take different approaches to address DIRECT’s design challenges imposed by memory requirements and data dependency. Three design aspects in topology, data structures, and task allocation are compared in detail. The goal is to analytically investigate the strengths and weaknesses of these parallel schemes, identify several key sources of inefficiency, and experimentally evaluate a number of improvements in the latest parallel DIRECT implementation. The performance studies demonstrate improved data structure efficiency and load balancing on a 2200 processor cluster.

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  1. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the Lamé system with applications in elastography (2020)
  2. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  3. Akman, Devin; Akman, Olcay; Schaefer, Elsa: Parameter estimation in ordinary differential equations modeling via particle swarm optimization (2018)
  4. Barkalov, Konstantin; Strongin, Roman: Solving a set of global optimization problems by the parallel technique with uniform convergence (2018)
  5. Bradford, Eric; Schweidtmann, Artur M.; Lapkin, Alexei: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm (2018)
  6. Campana, E. F.; Diez, M.; Liuzzi, G.; Lucidi, S.; Pellegrini, R.; Piccialli, V.; Rinaldi, F.; Serani, A.: A multi-objective \textbfDIRECTalgorithm for ship hull optimization (2018)
  7. Cancès, Eric (ed.); Friesecke, Gero (ed.); Helgaker, Trygve Ulf (ed.); Lin, Lin (ed.): Mathematical methods in quantum chemistry. Abstracts from the workshop held March 18--24, 2018 (2018)
  8. Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.: Filter-based DIRECT method for constrained global optimization (2018)
  9. Endres, Stefan C.; Sandrock, Carl; Focke, Walter W.: A simplicial homology algorithm for Lipschitz optimisation (2018)
  10. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  11. Mockus, Jonas; Paulavičius, Remigijus; Rusakevičius, Dainius; Šešok, Dmitrij; Žilinskas, Julius: Application of reduced-set Pareto-Lipschitzian optimization to truss optimization (2017)
  12. Scitovski, Rudolf: A new global optimization method for a symmetric Lipschitz continuous function and the application to searching for a globally optimal partition of a one-dimensional set (2017)
  13. Tao, Qinghua; Huang, Xiaolin; Wang, Shuning; Li, Li: Adaptive block coordinate DIRECT algorithm (2017)
  14. Baeyens, Enrique; Herreros, Alberto; Perán, José R.: A direct search algorithm for global optimization (2016)
  15. Barkalov, Konstantin; Gergel, Victor: Parallel global optimization on GPU (2016)
  16. Campana, Emilio F.; Diez, Matteo; Iemma, Umberto; Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco; Serani, Andrea: Derivative-free global ship design optimization using global/local hybridization of the DIRECT algorithm (2016)
  17. Di Pillo, G.; Liuzzi, G.; Lucidi, S.; Piccialli, V.; Rinaldi, F.: A DIRECT-type approach for derivative-free constrained global optimization (2016)
  18. Larson, Jeffrey; Wild, Stefan M.: A batch, derivative-free algorithm for finding multiple local minima (2016)
  19. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  20. Paulavičius, Remigijus; Žilinskas, Julius: Advantages of simplicial partitioning for Lipschitz optimization problems with linear constraints (2016)

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