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[null,"1.","Record Nr.",null,"UNINA9910139482503321"]
[null,null,"Titolo",null,"Advanced computational infrastructures for parallel and distributed adaptive applications / / edited by Manish Parashar, Xiaolin Li"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ, : John Wiley & Sons, 2010"]
[null,null,"ISBN",null,"9786612681721"]
[{"$nulls":4},"9781282681729"]
[{"$nulls":4},"1282681729"]
[{"$nulls":4},"9780470558027"]
[{"$nulls":4},"0470558024"]
[{"$nulls":4},"9780470558010"]
[{"$nulls":4},"0470558016"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (542 p.)"]
[null,null,"Collana",null,"Wiley series on parallel and distributed computing. ; ; v.66"]
[null,null,"Disciplina",null,"004.35"]
[{"$nulls":4},"004.36"]
[{"$nulls":4},"004.678"]
[null,null,"Soggetti",null,"Parallel processing (Electronic computers)"]
[{"$nulls":4},"Electronic data processing - Distributed processing"]
[{"$nulls":4},"Adaptive computing systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications; Contents; Preface; ACKNOWLEDGMENTS; Contributors; Biographies; 1. Introduction: Enabling Large-Scale Computational Science-Motivations, Requirements, and Challenges; Part I Adaptive Applications in Science and Engineering; 2. Adaptive Mesh Refinement MHD Simulations of Tokamak Refueling; 3. Parallel Computing Engines for Subsurface Imaging Technologies; 4. Plane Wave Seismic Data: Parallel and Adaptive Strategies for Velocity Analysis and Imaging"]
[{"$nulls":4},"5. Data-Directed Importance Sampling for Climate Model Parameter Uncertainty Estimation6. Adaptive Cartesian Methods for Modeling Airborne Dispersion; 7. Parallel and Adaptive Simulation of Cardiac Fluid Dynamics; 8. Quantum Chromodynamics on the BlueGene/L Supercomputer; Part II Adaptive Computational Infrastructures; 9. The SCIJump Framework for Parallel and Distributed Scientific Computing; "]
[{"$nulls":4},"10. Adaptive Computations in the Uintah Framework; 11. Managing Complexity in Massively Parallel, Adaptive, Multiphysics Finite Element Applications"]
[{"$nulls":4},"12. GrACE: Grid Adaptive Computational Engine for Parallel Structured AMR Applications13. Charm++ and AMPI: Adaptive Runtime Strategies via Migratable Objects; 14. The Seine Data Coupling Framework for Parallel Scientific Applications; Part III Dynamic Partitioning and Adaptive Runtime Management Frameworks; 15. Hypergraph-Based Dynamic Partitioning and Load Balancing; 16. Mesh Partitioning for Efficient Use of Distributed Systems; 17. Variable Partition Inertia: Graph Repartitioning and Load Balancing for Adaptive Meshes; 18. A Hybrid and Flexible Data Partitioner for Parallel SAMR"]
[{"$nulls":4},"19. Flexible Distributed Mesh Data Structure for Parallel Adaptive Analysis20. HRMS: Hybrid Runtime Management Strategies for Large-Scale Parallel Adaptive Applications; 21. Physics-Aware Optimization Method; 22. DistDLB: Improving Cosmology SAMR Simulations on Distributed Computing Systems Through Hierarchical Load Balancing; Index"]
[null,null,null,"Sommario/riassunto","A unique investigation of the state of the art in design, architectures, and implementations of advanced computational infrastructures and the applications they support Emerging large-scale adaptive scientific and engineering applications are requiring an increasing amount of computing and storage resources to provide new insights into complex systems. Due to their runtime adaptivity, these applications exhibit complicated behaviors that are highly dynamic, heterogeneous, and unpredictable-and therefore require full-fledged computational infrastructure support for problem solving, run"]