Geodesic
A scalable algorithm for solving non-stationary linear programming problems
Numerical methods and programming, Tome 19 (2018) no. 4, pp. 540-550.

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This paper is devoted to the scalability study of an NSLP algorithm for solving non-stationary high-dimension linear programming problems on cluster computing systems. The analysis is based on the BSF model of parallel computations. The BSF model is a new parallel computation model designed on the basis of BSP and SPMD models. The brief descriptions of the NSLP algorithm and the BSF model are given. The NSLP algorithm implementation in the form of a BSF program is considered. On the basis of the BSF cost metric, the upper bound of the NSLP algorithm scalability is derived and its parallel efficiency is estimated. The NSLP algorithm implementation using BSF skeleton is described. The scalability estimates obtained analytically and experimentally are compared.
Keywords: non-stationary high-dimension linear programming problem, NSLP algorithm, BSF parallel computation model, scalability estimation, cluster computing systems. 
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     author = {I. M. Sokolinskaya and L. B. Sokolinskii},
     title = {A scalable algorithm for solving non-stationary linear programming problems},
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     publisher = {mathdoc},
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     url = {http://geodesic.mathdoc.fr/item/VMP_2018_19_4_a19/}
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I. M. Sokolinskaya; L. B. Sokolinskii. A scalable algorithm for solving non-stationary linear programming problems. Numerical methods and programming, Tome 19 (2018) no. 4, pp. 540-550. http://geodesic.mathdoc.fr/item/VMP_2018_19_4_a19/