Combinatorial Scientific Computing

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Adjoint Calculation
Adjoint Code
advanced high-performance computing algorithms
Algorithm DelaunayRefinement
algorithmic differentiation
bipartite
Category=PBV
Category=UKC
Category=UMB
Coarser Graphs
Combinatorial Scientific Computing
computational fluid dynamics
Data Set
Delaunay Mesh
Delaunay Mesh Generation
Delaunay Refinement
Direct Solver
dissection
EE
Elimination Tree
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
graph
Graph Partitioning
Graph Partitioning Problem
hypergraph
Hypergraph Models
Hypergraph Partitioning
Independent Set
Interior - Point Methods
matrix-vector
multiplication
nested
Nested Dissection
network analysis methods
numerical optimisation
parallel computing
partitioning
scientific software tools
Seed Matrix
sparse
Sparse Matrices
Sparse Matrix
Sparsity Pattern
Tetrahedral Mesh
undirected
Vertex Separators

Product details

  • ISBN 9780367381752
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 15 Oct 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Uwe Naumann is an associate professor of computer science at RWTH Aachen University. Dr. Naumann has published more than 80 peer-reviewed papers and chaired several workshops. His research focuses on algorithmic differentiation, combinatorial graph algorithms, high-performance scientific computing, and the application of corresponding methods to real-world problems in computational science, engineering, and finance.

Olaf Schenk is an associate professor of computer science at the University of Lugano. Dr. Schenk has published more than 70 peer-reviewed book chapters, journal articles, and conference contributions. In 2008, he received an IBM Faculty Award on Cell Processors for Biomedical Hyperthermia Applications. His research interests include algorithmic and architectural problems in computational mathematics, scientific computing, and high-performance computing.