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Full Description
Combinatorial Scientific Computing explores the latest research on creating algorithms and software tools to solve key combinatorial problems on large-scale high-performance computing architectures. It includes contributions from international researchers who are pioneers in designing software and applications for high-performance computing systems.
The book offers a state-of-the-art overview of the latest research, tool development, and applications. It focuses on load balancing and parallelization on high-performance computers, large-scale optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications in large-scale social networks. The authors unify these seemingly disparate areas through a common set of abstractions and algorithms based on combinatorics, graphs, and hypergraphs.
Combinatorial algorithms have long played a crucial enabling role in scientific and engineering computations and their importance continues to grow with the demands of new applications and advanced architectures. By addressing current challenges in the field, this volume sets the stage for the accelerated development and deployment of fundamental enabling technologies in high-performance scientific computing.
Contents
Combinatorial Scientific Computing: Past Successes, Current Opportunities, Future Challenges. Combinatorial Problems in Solving Linear Systems. Combinatorial Preconditioners. Scalable Hybrid Linear Solvers. Combinatorial Problems in Algorithmic Differentiation. Combinatorial Problems in OpenAD. Getting Started with ADOL-C. Algorithmic Differentiation and Nonlinear Optimization for an Inverse Medium Problem. Combinatorial Aspects/Algorithms in Computational Fluid Dynamics. Unstructured Mesh Generation. 3D Delaunay Mesh Generation. Two-Dimensional Approaches to Sparse Matrix Partitioning. Parallel Partitioning, Ordering, and Coloring in Scientific Computing. Scotch and PT-Scotch Graph Partitioning Software: An Overview. Massively Parallel Graph Partitioning: A Case in Human Bone Simulations. Algorithmic and Statistical Perspectives on Large-Scale Data Analysis. Computational Challenges in Emerging Combinatorial Scientific Computing Applications. Spectral Graph Theory. Algorithms for Visualizing Large Networks.
