High performance computing on complex environments / Emmanuel Jeannot, Julius Zilinskas.

By: Jeannot, EmmanuelContributor(s): Žilinskas, J. (Julius), 1973-Material type: TextTextSeries: Wiley series on parallel and distributed computingPublisher: Hoboken, New Jersey : John Wiley & Sons, [2014]Description: 1 online resource (xxi, 395 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781118712078; 1118712072; 9781118866672; 1118866673; 9781118711897; 1118711890; 1306638712; 9781306638715Subject(s): High performance computing | High performance computing | COMPUTERS -- Computer Literacy | COMPUTERS -- Computer Science | COMPUTERS -- Data Processing | COMPUTERS -- Hardware -- General | COMPUTERS -- Information Technology | COMPUTERS -- Machine Theory | COMPUTERS -- Reference | High performance computing | High performance computingGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: High performance computing on complex environments.DDC classification: 004.1/1 LOC classification: QA76.88Online resources: Wiley Online Library
Contents:
Series; Title Page; Copyright; Dedication; Contributors; Preface; 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; 1.3 Activities of The project; 1.4 Main Outcomes of the Action; 1.5 Contents of The Book; Acknowledgment; Part II: Numerical Analysis for Heterogeneous and Multicore Systems; Chapter 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 Preconditioning2.3 Preconditioning Techniques; 2.4 Defect-Correction Technique; 2.5 Multigrid Method; 2.6 Parallelization of Iterative Methods; 2.7 Heterogeneous Systems; 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; 3.3 Parallel Implementation; 3.4 Results; 3.5 Discussion; 3.6 Conclusion; Acknowledgment; References
Chapter 4: Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience4.1 Introduction; 4.2 Formulation of the Discrete Model; 4.3 Parallel Algorithms; 4.4 Computational Results; 4.5 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; 5.3 Formalization of the Problem; 5.4 Algorithmic Strategies for Topology Mapping; 5.5 Mapping Enforcement Techniques; 5.6 Survey of Solutions
5.7 Conclusion and Open ProblemsAcknowledgment; References; Chapter 6: Optimization of Collective Communication for Heterogeneous HPC Platforms; 6.1 Introduction; 6.2 Overview of Optimized Collectives and Topology-Aware Collectives; 6.3 Optimizations of Collectives on Homogeneous Clusters; 6.4 Heterogeneous Networks; 6.5 Topology- and Performance-Aware Collectives; 6.6 Topology as Input; 6.7 Performance as Input; 6.8 Non-MPI Collective Algorithms for Heterogeneous Networks; 6.9 Conclusion; Acknowledgments; References; Chapter 7: Effective Data Access Patterns on Massively Parallel Processors
7.1 Introduction7.2 Architectural Details; 7.3 K-Model; 7.4 Parallel Prefix Sum; 7.5 Bitonic Sorting Networks; 7.6 Final Remarks; Acknowledgments; References; Chapter 8: Scalable Storage I/O Software for Blue Gene Architectures; 8.1 Introduction; 8.2 Blue Gene System Overview; 8.3 Design and Implementation; 8.4 Conclusions and Future Work; Acknowledgments; References; Part IV: Efficient Exploitation of Heterogeneous Architectures; Chapter 9: Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems; 9.1 Introduction; 9.2 Concurrent Workflow Scheduling
Summary: With recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. This book provides recent research results in high-performance computing on complex environments, information on how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems, detailed studies on the impact of applying heterogeneous computing practices to real problems, and applications varying from remote sensing to tomography. Topics include: numerical analysis for heterogeneous and multicore systems; optimization of communication for high performance heterogeneous and hierarchical platforms; efficient exploitation of heterogeneous architectures, hybrid CPU+GPU, and distributed systems; energy awareness in high-performance computing; and applications of heterogeneous high-performance computing. -- Edited summary from book.
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Includes bibliographical references and index.

Print version record and CIP data provided by publisher.

With recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. This book provides recent research results in high-performance computing on complex environments, information on how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems, detailed studies on the impact of applying heterogeneous computing practices to real problems, and applications varying from remote sensing to tomography. Topics include: numerical analysis for heterogeneous and multicore systems; optimization of communication for high performance heterogeneous and hierarchical platforms; efficient exploitation of heterogeneous architectures, hybrid CPU+GPU, and distributed systems; energy awareness in high-performance computing; and applications of heterogeneous high-performance computing. -- Edited summary from book.

Series; Title Page; Copyright; Dedication; Contributors; Preface; 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; 1.3 Activities of The project; 1.4 Main Outcomes of the Action; 1.5 Contents of The Book; Acknowledgment; Part II: Numerical Analysis for Heterogeneous and Multicore Systems; Chapter 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 Preconditioning2.3 Preconditioning Techniques; 2.4 Defect-Correction Technique; 2.5 Multigrid Method; 2.6 Parallelization of Iterative Methods; 2.7 Heterogeneous Systems; 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; 3.3 Parallel Implementation; 3.4 Results; 3.5 Discussion; 3.6 Conclusion; Acknowledgment; References

Chapter 4: Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience4.1 Introduction; 4.2 Formulation of the Discrete Model; 4.3 Parallel Algorithms; 4.4 Computational Results; 4.5 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; 5.3 Formalization of the Problem; 5.4 Algorithmic Strategies for Topology Mapping; 5.5 Mapping Enforcement Techniques; 5.6 Survey of Solutions

5.7 Conclusion and Open ProblemsAcknowledgment; References; Chapter 6: Optimization of Collective Communication for Heterogeneous HPC Platforms; 6.1 Introduction; 6.2 Overview of Optimized Collectives and Topology-Aware Collectives; 6.3 Optimizations of Collectives on Homogeneous Clusters; 6.4 Heterogeneous Networks; 6.5 Topology- and Performance-Aware Collectives; 6.6 Topology as Input; 6.7 Performance as Input; 6.8 Non-MPI Collective Algorithms for Heterogeneous Networks; 6.9 Conclusion; Acknowledgments; References; Chapter 7: Effective Data Access Patterns on Massively Parallel Processors

7.1 Introduction7.2 Architectural Details; 7.3 K-Model; 7.4 Parallel Prefix Sum; 7.5 Bitonic Sorting Networks; 7.6 Final Remarks; Acknowledgments; References; Chapter 8: Scalable Storage I/O Software for Blue Gene Architectures; 8.1 Introduction; 8.2 Blue Gene System Overview; 8.3 Design and Implementation; 8.4 Conclusions and Future Work; Acknowledgments; References; Part IV: Efficient Exploitation of Heterogeneous Architectures; Chapter 9: Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems; 9.1 Introduction; 9.2 Concurrent Workflow Scheduling

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