High-Performance Computing on Complex Environments.Material type: TextSeries: eBooks on DemandWiley Series on Parallel and Distributed Computing: Publisher: Hoboken : Wiley, 2014Description: 1 online resource (502 p.)ISBN: 9781118866672Subject(s): High performance computing | Parallel processing (Electronic computers) | VisualizationGenre/Form: Electronic books.Additional physical formats: Print version:: High-Performance Computing on Complex EnvironmentsDDC classification: 004.11 LOC classification: QA76.88 .J43 2014Online resources: Click here to view this ebook.
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|Electronic Book||UT Tyler Online Online||QA76.88 .J43 2014 (Browse shelf)||http://uttyler.eblib.com/patron/FullRecord.aspx?p=1666528||Available||EBL1666528|
Cover; Title Page; Contents; Contributors; Preface; European Science Foundation; Part I Introduction; Chapter 1 Summary of the Open European Network for High-Performance Computing in Complex Environments; 1.1 Introduction and Vision; 1.2 Scientific Organization; 1.2.1 Scientific Focus; 1.2.2 Working Groups; 1.3 Activities of the Project; 1.3.1 Spring Schools; 1.3.2 International Workshops; 1.3.3 Working Groups Meetings; 1.3.4 Management Committee Meetings; 1.3.5 Short-Term Scientific Missions; 1.4 Main Outcomes of the Action; 1.5 Contents of the Book; Acknowledgment
Part II Numerical Analysis for Heterogeneous and Multicore SystemsChapter 2 On the Impact of the Heterogeneous Multicore and Many-Core Platforms on Iterative Solution Methods and Preconditioning Techniques; 2.1 Introduction; 2.2 General Description of Iterative Methods and Preconditioning; 2.2.1 Basic Iterative Methods; 2.2.2 Projection Methods: CG and GMRES; 2.3 Preconditioning Techniques; 2.4 Defect-Correction Technique; 2.5 Multigrid Method; 2.6 Parallelization of Iterative Methods; 2.7 Heterogeneous Systems; 2.7.1 Heterogeneous Computing
2.7.2 Algorithm Characteristics and Resource Utilization2.7.3 Exposing Parallelism; 2.7.4 Heterogeneity in Matrix Computation; 2.7.5 Setup of Heterogeneous Iterative Solvers; 2.8 Maintenance and Portability; 2.9 Conclusion; Acknowledgments; References; Chapter 3 Efficient Numerical Solution of 2D Diffusion Equation on Multicore Computers; 3.1 Introduction; 3.2 Test Case; 3.2.1 Governing Equations; 3.2.2 Solution Procedure; 3.3 Parallel Implementation; 3.3.1 Intel PCM Library; 3.3.2 OpenMP; 3.4 Results; 3.4.1 Results of Numerical Integration; 3.4.2 Parallel Efficiency; 3.5 Discussion
3.6 ConclusionAcknowledgment; References; Chapter 4 Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience; 4.1 Introduction; 4.2 Formulation of the Discrete Model; 4.2.1 The theta-Implicit Discrete Scheme; 4.2.2 The Predictor--Corrector Algorithm I; 4.2.3 The Predictor--Corrector Algorithm II; 4.3 Parallel Algorithms; 4.3.1 Parallel theta-Implicit Algorithm; 4.3.2 Parallel Predictor--Corrector Algorithm I; 4.3.3 Parallel Predictor--Corrector Algorithm II; 4.4 Computational Results; 4.4.1 Experimental Comparison of Predictor--Corrector Algorithms
4.4.2 Numerical Experiment of Neuron Excitation4.5 Conclusions; Acknowledgments; References; Part III Communication and Storage Considerations in High-Performance Computing; Chapter 5 An Overview of Topology Mapping Algorithms and Techniques in High-Performance Computing; 5.1 Introduction; 5.2 General Overview; 5.2.1 A Key to Scalability: Data Locality; 5.2.2 Data Locality Management in Parallel Programming Models; 5.2.3 Virtual Topology: Definition and Characteristics; 5.2.4 Understanding the Hardware; 5.3 Formalization of the Problem; 5.4 Algorithmic Strategies for Topology Mapping
5.4.1 Greedy Algorithm Variants
Covers cutting-edge research in HPC on complex environments, following an international collaboration of members of the ComplexHPC <br /> Explains how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems<br /> Twenty-three chapters and over 100 illustrations cover domains such as numerical analysis, communication and storage, applications, GPUs and accelerators, and energy efficiency
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Author notes provided by Syndetics
EMMANUEL JEANNOT is a Senior Research Scientist at INRIA. He received his PhD in computer science from École Normale Supérieur de Lyon. His main research interests are processes placement, scheduling for heterogeneous environments and grids, data redistribution, algorithms, and models for parallel machines.
JULIUS ILINSKAS is a Principal Researcher and a Head of Department at Vilnius University, Lithuania. His research interests include parallel computing, optimization, data analysis, and visualization.