On the Parallel Strategies in Mathematical Modeling

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2 Citations (Scopus)

Abstract

The article considers parallel strategies and tactics at different stages of mathematical modeling. These technological steps include geometrical and functional modeling, discretization and approximation, algebraic solvers and optimization methods for inverse problems, postprocessing and visualization of numerical results, as well as decision-making systems. Scalable parallelism can be provided by combined application of MPI tools, multi-thread computing, vectorization, and the use of graphics accelerators. The general method to achieve high-performance computing consists in minimizing data communications, which are the most time and energy consuming. The construction of efficient parallel algorithms and code optimization is based on various approaches at different levels of computational schemes. The implementation of the biggest interdisciplinary direct and inverse problems in cloud computing technologies is considered. The corresponding applied software with a long life cycle is represented as integrated environment oriented to large groups of end users.

Original languageEnglish
Title of host publicationParallel Computational Technologies - 11th International Conference, PCT 2017, Revised Selected Papers
EditorsL Sokolinsky, M Zymbler
PublisherSpringer-Verlag GmbH and Co. KG
Pages73-85
Number of pages13
Volume753
ISBN (Print)9783319670348
DOIs
Publication statusPublished - 1 Jan 2017
Event11th International Conference on Parallel Computational Technologies, PCT 2017 - Kazan, Russian Federation
Duration: 3 Apr 20177 Apr 2017

Publication series

NameCommunications in Computer and Information Science
Volume753
ISSN (Print)1865-0929

Conference

Conference11th International Conference on Parallel Computational Technologies, PCT 2017
CountryRussian Federation
CityKazan
Period03.04.201707.04.2017

Keywords

  • Accelerators
  • Communications
  • Domain decomposition
  • Exchange buffers
  • Hierarchical memory
  • Multi-thread computing
  • Runtime
  • Scalable parallelism
  • Speedup
  • Vectorization

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