1. Abramov S.M. Analiz razvitiya superkomp'yuternoj otrasli v Rossii i v mire // Programmnye sistemy: teoriya i prilozheniya. Institut programmnykh sistem im. A.K. Ajlamazyana Rossijskoj akademii nauk. 2019, № 3(42).
2. Makarov V.L., Bakhtizin A.R., Sushko E.D. Mul'tiagentnye sistemy i superkomp'yuternye tekhnologii v obschestvennykh naukakh // Nejrokomp'yutery: razrabotka, primenenie. Radiotekhnika. 2017, № 5.
3. Makarov V.L., Bakhtizin A.R., Sushko E.D., Sushko G.B. Modelirovanie sotsial'nykh protsessov na superkomp'yuterakh: novye tekhnologii // Vestnik RAN. 2018, Tom 88, №6.
4. Makarov V.L., Bakhtizin A.R., Sushko E.D., Sushko G.B. Razrabotka agent-orientirovannoj demograficheskoj modeli Rossii i ee superkomp'yuternaya realizatsiya // Trudy mezhdunarodnoj konferentsii. Superkomp'yuternyj konsortsium universitetov Rossii, Rossijskaya akademiya nauk. 2018. S. 758-769.
5. Okrepilov V.V., Makarov V.L., Bakhtizin A.R., Kuz'mina S.N. Primenenie superkomp'yuternykh tekhnologij dlya modelirovaniya sotsial'no-ehkonomicheskikh sistem // Ehkonomika regiona. Institut ehkonomiki Ural'skogo otdeleniya RAN. 2015, № 2 (42).
6. Rakitskij A.A., Ryabko B.Ya. Teoretiko-informatsionnyj podkhod k otsenke proizvoditel'nosti superkomp'yuterov // Vychislitel'nye tekhnologii. Institut vychislitel'nykh tekhnologij Sibirskogo otdeleniya RAN. 2018, № 1.
7. Bellman R. Adaptive Control Processes: A Guided Tour // Rand Corporation. Research studies. Princeton University Press. 1961.
8. Brumm J., Mikushin D., Scheidegger S., Schenk O. Scalable high-dimensional dynamic stochastic economic modeling // Journal of Computational Science. 2015, vol. 11.
9. Brumm J., Scheidegger S. Using adaptive sparse grids to solve high-dimensional dynamic models // Econometrica. 2017, vol. 85, № 5.
10. Bungartz H.-J., Dirnstorfer S. Multivariate quadrature on adaptive sparse grids // Computing. 2003, vol. 71.
11. Bungartz H.-J., Griebel M., Sparse grids // Acta Numerica. 2004, vol. 13.
12. David K. L. J., Bizer S. Taxation and uncertainty // The American Economic Review. 1989, vol. 79, №. 2.
13. Diamond P. A. National debt in a neoclassical growth model // American Economic Review. 1965, vol. 55, №. 5
14. Garcke J., Griebel M. Sparse Grids and Applications // Lecture Notes in Computational Science and Engineering. Springer. 2012.
15. Jones M.T. Optimizing supercomputer resource management with SLURM // IBM. URL: https://www.ibm.com/developerworks/ru/library/l-slurm-utility/index.html
16. Judd K. L. Numerical methods in economics // The MIT press. 1998.
17. Ma X., Zabaras N. An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations // J. Comput. Phys. 2009, vol. 228, № 8.
18. Pfluger D. Spatially adaptive refinement // In Sparse Grids and Applications, ser. Lecture Notes in Computational Science and Engineering, Garcke J., Griebel M. Eds. Berlin Heidelberg: Springer. 2012. pp. 243–262.
19. Reinders J. Intel Threading Building Blocks // 1st ed. Sebastopol, CA, USA: O’Reilly & Associates, Inc. 2007.
20. Scheidegger S., Mikushin D., K?ubler F., Schenk O. Rethinking large-scale economic modeling for efficiency: optimizations for GPU and Xeon Phi clusters // IEEE International Parallel and Distributed Processing Symposium. 2018. pp. 610-619.
21. Skjellum A., Gropp W., Lusk E. Using MPI // MIT Press. 1999.
Comments
No posts found