The topic of the presented paper is a promising approach to achieve optimal Design of Experiment (DoE), i.e. spreading of points within a design domain, using a simulation of a discrete dynamical system of interacting particles within an n-dimensional design space. The system of mutually repelling particles represents a physical analogy of the Audze-Eglājs (AE) optimization criterion and its periodical modification (PAE), respectively. The paper compares the performance of two approaches to implementation: a single-thread process using the JAVA language environment and a massively parallel solution employing the nVidia CUDA platform.
 IMAN, R. L.; CONOVER, W. J. Small sample sensitivity analysis techniques for computer models with an application to risk assessment. Communications in statistics-theory and methods, 1980, 9.17: 1749-1842.
 VOŘECHOVSKÝ, M.; NOVÁK, D. Correlation control in small-sample Monte Carlo type simulations I: A simulated annealing approach. Probabilistic Engineering Mechanics, 2009, 24.3: 452-462.
 BATES, S. J.; SIENZ, J.; LANGLEY, D. S. Formulation of the Audze-Eglais uniform Latin hypercube design of experiments. Advances in Engineering Software, 2003, 34.8: 493-506.
 TOROPOV, S.B., QUERIN, O. Proceedings of Ninth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, 2007, 1-12.
 AUDZE, P.; EGLAIS, V. New approach for planning out of experiments. Problems of Dynamics and Strengths, 1977, 35: 104-107.
 JOHNSON, M. E.; MOORE, L. M.; YLVISAKER, D. Minimax and maximin distance designs. Journal of statistical planning and inference, 1990, 26.2: 131-148.
 FANG, K. T.; WANG, Y. Number-Theoretic Methods in Statistics, Chapman & Hall. New York, 1994.
 FANG, K.; MA, Ch; WINKER, P.. Centered L₂-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs. Mathematics of Computation, 2002, 71.237: 275-296.
 FANG, K.; MA, Ch. Wrap-around L 2-discrepancy of random sampling, Latin hypercube and uniform designs. Journal of complexity, 2001, 17.4: 608-624.
 ROMERO, V.J., BURKHARDT, J.V., GUNZBURGER, M.D., PETERSON J.S. Reliability Engineering and System Safety, The Fourth International Conference on Sensitivity Analysis of Model Output, 2006, 91: 10-11.
 ŠMÍDOVÁ, M. Performance comparison of methods for design of experiments for analysis of tasks involving random variables. Brno University of Technology, 2014
 VOŘECHOVSKÝ, M. Hierarchical refinement of Latin hypercube samples. Computer‐Aided Civil and Infrastructure Engineering, 2015, 30.5: 394-411.
 VOŘECHOVSKÝ, M.; ELIÁŠ, J. Improved formulation of Audze-Eglājs criterion for spacefilling designs. 12th International Conference on Applications of Statistics and Probability in Civil Engineering, 2015.
 FRANTÍK, P., VOŘECHOVSKÝ, M. Modification of the FyDiK n-D application for dynamical simulations of discrete dynamical particle systems in JAVA environment.
 CHE, S., et al. A performance study of general-purpose applications on graphics processors using CUDA. Journal of parallel and distributed computing, 2008, 68.10: 1370-1380.
 BAYOUMI, A., et al. Scientific and engineering computing using ATI stream technology. Computing in Science & Engineering, 2009, 11.6: 92-97.
 FRANTÍK, P.; VESELÝ, V.; KERŠNER, Z. Parallelization of lattice modelling for estimation of fracture process zone extent in cementitious composites. Advances in Engineering Software, 2013, 60: 48-57.