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.
[1]
Rob H. Bisseling,et al.
Parallel hypergraph partitioning for scientific computing
,
2006,
Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.
[2]
Ilya Safro,et al.
Advances in parallel partitioning, load balancing and matrix ordering for scientific computing
,
2009
.
[3]
Rob H. Bisseling,et al.
Abusing a hypergraph partitioner for unweighted graph partitioning
,
2012,
Graph Partitioning and Graph Clustering.
[4]
Igor L. Markov,et al.
Hypergraph Partitioning and Clustering
,
2007,
Handbook of Approximation Algorithms and Metaheuristics.
[5]
I. Duff.
A survey of sparse matrix research
,
1977,
Proceedings of the IEEE.