Parallel algorithms in computational science and engineering
This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations for a variety of kern...
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| Weitere Verfasser: | , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham, Switzerland
Birkhäuser
2020
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| Schriftenreihe: | Modeling and Simulation in Science, Engineering and Technology
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Tag hinzufügen | Zusammenfassung: | This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations for a variety of kernels that arise in applications. The rest of the volume focuses on state-of-the-art applications from diverse domains. By structuring the volume around these two areas, it presents a comprehensive view of the application landscape for high-performance computing, while also enabling readers to develop new applications using the kernels. Readers will learn how to choose the most suitable parallel algorithms for any given application, ensuring that theory and practicality are clearly connected. Applications using these techniques are illustrated in detail, including: Computational materials science and engineering Computational cardiovascular analysis Multiscale analysis of wind turbines and turbomachinery Weather forecasting Machine learning techniques Parallel Algorithms in Computational Science and Engineering will be an ideal reference for applied mathematicians, engineers, computer scientists, and other researchers who utilize high-performance computing in their work. Intro -- Preface -- Part 1: High-Performance Algorithms -- Part 2: High-Performance Computational Science and Engineering Applications -- Contents -- Part I High Performance Algorithms -- State-of-the-Art Sparse Direct Solvers -- 1 Introduction -- 2 Maximum Weight Matching -- 3 Symbolic Symmetric Reordering Techniques -- 3.1 Multilevel Nested Dissection -- 3.1.1 Coarsening Phase -- 3.1.2 Partitioning Phase -- 3.1.3 Uncoarsening Phase -- 3.2 Other Reordering Methods -- 4 Sparse LU Decomposition -- 4.1 The Elimination Tree -- 4.2 The Supernodal Approach -- 5 Sparse Direct Solvers-Supernodal Data Structures -- 6 Application-Circuit Simulation -- References -- The Effect of Various Sparsity Structures on Parallelism and Algorithms to Reveal Those Structures -- 1 Introduction -- 2 Preliminaries -- 2.1 Graph Partitioning by Vertex Separator (GPVS) -- 2.2 Hypergraph Partitioning (HP) -- 2.3 Matrix Definitions -- 3 Singly-Bordered Block-Diagonal Form -- 4 Doubly-Bordered Block-Diagonal Form -- 5 Nonempty Off-Diagonal Block Minimization -- 6 Block-Diagonal Form with Overlap -- 6.1 BDO Form -- 6.2 BDCO Form -- 7 Conclusions -- References -- Structure-Exploiting Interior Point Methods -- 1 Introduction -- 1.1 Notation -- 2 IP Algorithm -- 2.1 Search Direction Computation -- 2.2 Backtracking Line-Search Filter Method -- 2.3 Inertia Correction and Curvature Detection -- 2.4 Barrier Parameter Update Strategy -- 2.4.1 Monotone and Adaptive Strategies -- 2.4.2 Mehrotra's Predictor-Corrector -- 2.4.3 Quality Function -- 2.5 Problem Scaling and Convergence Criteria -- 3 IP Methods for OPF Problems -- 3.1 Optimal Power Flow -- 3.2 Structure-Exploiting IP Methods-Security Constrained and Multiperiod OPF -- 3.3 Impact of Slack Variables Elimination -- 4 Structure-Exploiting Solution Strategies for IP Optimization. |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xii, 417 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030437350 978-3-030-43735-0 |