Parallel Processing for Scientific Computing

Michael A. Heroux is the Solvers Project Leader at Sandia National Laboratory; his work focuses on new algorithm development and robust parallel implementation of solver components. He leads development of the Trilinos Project, an effort to provide solution methods in a state-of-the-art software framework. He also maintains an active interest in the interaction between scientific/engineering applications and high-performance computer architectures. Padma Raghavan is a Professor in the Department of Computer Science and Engineering at Pennsylvania State University. Her research interests include parallel and distributed computing, sparse matrix graph techniques and their applications, and software environments and component architectures for large-scale computational materials science. Horst D. Simon is Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory. His recursive spectral bisection algorithm is regarded as a breakthrough in parallel algorithms for unstructured computations, and he was honored for his algorithm research efforts with the 1988 Gordon Bell Prize for parallel processing research.

List of Figures List of Tables Preface Chapter 1: Frontiers of Scientific Computing: An Overview Part I: Performance Modeling, Analysis and Optimization. Chapter 2: Performance Analysis: From Art to Science Chapter 3: Approaches to Architecture-Aware Parallel Scientific Computation Chapter 4: Achieving High Performance on the BlueGene/L Supercomputer Chapter 5: Performance Evaluation and Modeling of Ultra-Scale Systems Part II: Parallel Algorithms and Enabling Technologies. Chapter 6: Partitioning and Load Balancing Chapter 7: Combinatorial Parallel and Scientific Computing Chapter 8: Parallel Adaptive Mesh Refinement Chapter 9: Parallel Sparse Solvers, Preconditioners, and Their Applications Chapter 10: A Survey of Parallelization Techniques for Multigrid Solvers Chapter 11: Fault Tolerance in Large-Scale Scientific Computing Part III: Tools and Frameworks for Parallel Applications. Chapter 12: Parallel Tools and Environments: A Survey Chapter 13: Parallel Linear Algebra Software Chapter 14: High-Performance Component Software Systems Chapter 15: Integrating Component-Based Scientific Computing Software Part IV: Applications of Parallel Computing. Chapter 16: Parallel Algorithms for PDE-Constrained Optimization Chapter 17: Massively Parallel Mixed-Integer Programming Chapter 18: Parallel Methods and Software for Multicomponent Simulations Chapter 19: Parallel Computational Biology Chapter 20: Opportunities and Challenges for Parallel Computing in Science and Engineering Index.