Parallel Algorithms in Computational Science and Engineering

1. Front Matter

   Pages i-xii

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2. High Performance Algorithms

   1. Front Matter

      Pages 1-1

      PDF

   2. State-of-the-Art Sparse Direct Solvers

      • Matthias Bollhöfer, Olaf Schenk, Radim Janalik, Steve Hamm, Kiran Gullapalli

      Pages 3-33

   3. The Effect of Various Sparsity Structures on Parallelism and Algorithms to Reveal Those Structures

      • Oguz Selvitopi, Seher Acer, Murat Manguoğlu, Cevdet Aykanat

      Pages 35-62

   4. Structure-Exploiting Interior Point Methods

      • Juraj Kardoš, Drosos Kourounis, Olaf Schenk

      Pages 63-93

   5. Parallel Hybrid Sparse Linear System Solvers

      • Murat Manguoğlu, Eric Polizzi, Ahmed H. Sameh

      Pages 95-120

3. High Performance Computational Science and Engineering Applications

   1. Front Matter

      Pages 121-121

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   2. Computational Materials Science and Engineering

      • Eric Polizzi, Yousef Saad

      Pages 123-150

   3. Computational Cardiovascular Analysis with the Variational Multiscale Methods and Isogeometric Discretization

      • Thomas J. R. Hughes, Kenji Takizawa, Yuri Bazilevs, Tayfun E. Tezduyar, Ming-Chen Hsu

      Pages 151-193

   4. ALE and Space–Time Variational Multiscale Isogeometric Analysis of Wind Turbines and Turbomachinery

      • Yuri Bazilevs, Kenji Takizawa, Tayfun E. Tezduyar, Ming-Chen Hsu, Yuto Otoguro, Hiroki Mochizuki et al.

      Pages 195-233

   5. Variational Multiscale Flow Analysis in Aerospace, Energy and Transportation Technologies

      • Kenji Takizawa, Yuri Bazilevs, Tayfun E. Tezduyar, Artem Korobenko

      Pages 235-280

   6. Multiscale Crowd Dynamics Modeling and Safety Problems Towards Parallel Computing

      • Bouchra Aylaj, Nicola Bellomo

      Pages 281-295

   7. HPC for Weather Forecasting

      • John Michalakes

      Pages 297-323

   8. A Simple Study of Pleasing Parallelism on Multicore Computers

      • Yanfei Ren, David F. Gleich

      Pages 325-346

   9. Parallel Fast Time-Domain Integral-Equation Methods for Transient Electromagnetic Analysis

      • Yang Liu, Eric Michielssen

      Pages 347-379

   10. Parallel Optimization Techniques for Machine Learning

       • Sudhir Kylasa, Chih-Hao Fang, Fred Roosta, Ananth Grama

       Pages 381-417

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.

• High-performance computing
• HPC
• HPC computing
• Compute kernel
• Parallel Algorithms
• Sparse Matrix Computations
• Data science high performance computing
• Large-scale optimization
• Computational biology
• Materials Science and Engineering
• Fluid-structure interaction
• Computational Medicine
• Parallel Computing
• Computational Science and Engineering
• Computational Mechanics
• Computational Fluid Mechanics
• Computational Nanoelectronics
• Microelectromechanical Systems
• Supercomputer
• Supercomputer algorithm

• Purdue University, West Lafayette, USA

  Ananth Grama, Ahmed H. Sameh

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