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. Barkalov, Konstantin; Strongin, Roman: Solving a set of global optimization problems by the parallel technique with uniform convergence (2018)
  2. Mockus, Jonas; Paulavičius, Remigijus; Rusakevičius, Dainius; Šešok, Dmitrij; Žilinskas, Julius: Application of reduced-set Pareto-Lipschitzian optimization to truss optimization (2017)
  3. 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)
  4. Tao, Qinghua; Huang, Xiaolin; Wang, Shuning; Li, Li: Adaptive block coordinate DIRECT algorithm (2017)
  5. Barkalov, Konstantin; Gergel, Victor: Parallel global optimization on GPU (2016)
  6. 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)
  7. Di Pillo, G.; Liuzzi, G.; Lucidi, S.; Piccialli, V.; Rinaldi, F.: A DIRECT-type approach for derivative-free constrained global optimization (2016)
  8. Larson, Jeffrey; Wild, Stefan M.: A batch, derivative-free algorithm for finding multiple local minima (2016)
  9. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  10. Paulavičius, Remigijus; Žilinskas, Julius: Advantages of simplicial partitioning for Lipschitz optimization problems with linear constraints (2016)
  11. Turkalj, Željko; Markulak, Damir; Singer, Slavica; Scitovski, Rudolf: Research project grouping and ranking by using adaptive Mahalanobis clustering (2016)
  12. Custódio, A. L.; Madeira, J. F. A.: GLODS: global and local optimization using direct search (2015)
  13. Di Pillo, Gianni; Lucidi, Stefano; Rinaldi, Francesco: A derivative-free algorithm for constrained global optimization based on exact penalty functions (2015)
  14. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the linear wave equation (2015)
  15. Easterling, David R.; Watson, Layne T.; Madigan, Michael L.; Castle, Brent S.; Trosset, Michael W.: Parallel deterministic and stochastic global minimization of functions with very many minima (2014)
  16. Hamilton, Sarah Jane; Hauptmann, Andreas; Siltanen, Samuli: A data-driven edge-preserving D-bar method for electrical impedance tomography (2014)
  17. Liu, Qunfeng; Cheng, Wanyou: A modified DIRECT algorithm with bilevel partition (2014)
  18. Sabo, Kristian; Scitovski, Rudolf: Interpretation and optimization of the $k$-means algorithm. (2014)
  19. Grbić, Ratko; Nyarko, Emmanuel Karlo; Scitovski, Rudolf: A modification of the DIRECT method for Lipschitz global optimization for a symmetric function (2013)
  20. Di Pillo, G.; Lucidi, S.; Rinaldi, F.: An approach to constrained global optimization based on exact penalty functions (2012)

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