## Parallel Processing for Scientific ComputingParallel Processing for Scientific Computing is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, scientists, and computer scientists focus on to make parallel processing effective for scientific problems. It is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth looks at applications that require parallel computing for scaling to solve larger and more realistic models that can advance science and engineering. In sum, this is an up-to-date reference for researchers and application developers on the state of the art in scientific computing. It also serves as an excellent overview and introduction, especially for students interested in computational modeling and simulation. |

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### Contents

San Diego Supercomputer Center Mathematics and Computer | 1 |

Bruce Curtis Troy NY 12180 USA omarices texas | 9 |

Rochester MN 55901 | 31 |

Approaches to ArchitectureAware Parallel Scientific Computation | 33 |

Rob Armstrong gyanus ibm com California Institute of Technology | 45 |

Travis Desell Laboratory | 49 |

Michael A Heroux Salt Lake City UT 84112 USA School of Computer Science | 52 |

Albuquerque NM 87185 USA Ian Foster Yorktown Heights NY 10598 | 55 |

Lawrence Livermore National | 147 |

Parallel Sparse Solvers Preconditioners and Their Applications | 163 |

A Survey of Parallelization Techniques for Multigrid Solvers | 179 |

Fault Tolerance in LargeScale Scientific Computing | 203 |

A Survey | 223 |

Parallel Linear Algebra Software | 233 |

HighPerformance Component Software Systems | 249 |

Integrating ComponentBased Scientific Computing Software | 271 |

Achieving High Performance on the BlueGeneL Supercomputer | 59 |

P O Box 808 L561 | 75 |

Performance Evaluation and Modeling of UltraScale Systems | 77 |

Lawrence Berkeley National | 78 |

Partitioning and Load Balancing for Emerging Parallel Applications | 99 |

Combinatorial Parallel and Scientific Computing | 127 |

Parallel Adaptive Mesh Refinement | 143 |

### Common terms and phrases

achieved adaptive adaptive mesh refinement application approach architecture bandwidth behavior benchmarks BG/L nodes cache capabilities chapter checkpointing clusters communication component Computer Science Conference on Parallel coprocessor decomposition developed DFPU Distributed Computing domain dynamic load balancing efficient environment equations example execution factorization Figure finite element framework function global graph partitioning grid hardware heterogeneous hierarchical High Performance hypergraph IEEE implementation interface iterative load balancing mapping matrix mechanism memory mesh methods metrics minimize MPI tasks multigrid multigrid methods multilevel number of processors OpenMP operations optimization Parallel and Distributed parallel computing Parallel Processing parallel programming parameters Paraver partitioners performance analysis performance models Power4 preconditioner problem Proc provides refinement requires routine runtime SAMR scalability Scientific Computing SIMD simulation solution solvers solving sparse sparse matrix statistic strategies Supercomputing techniques TERESCO threads tion torus trace unstructured vector virtual node mode