Coverart for item
The Resource Exascale scientific applications : scalability and performance portability, edited by Tjerk P. Straatsma, Timothy J. Williams, Katerina Antypas

Exascale scientific applications : scalability and performance portability, edited by Tjerk P. Straatsma, Timothy J. Williams, Katerina Antypas

Label
Exascale scientific applications : scalability and performance portability
Title
Exascale scientific applications
Title remainder
scalability and performance portability
Statement of responsibility
edited by Tjerk P. Straatsma, Timothy J. Williams, Katerina Antypas
Contributor
Subject
Language
eng
Dewey number
502.85
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Straatsma, Tjerk P.,
  • Williams, Timothy J.
  • Antypas, Katerina,
Series statement
Chapman & Hall/CRC computational science
http://library.link/vocab/subjectName
  • Science
  • Technology
  • Exascale computing
Label
Exascale scientific applications : scalability and performance portability, edited by Tjerk P. Straatsma, Timothy J. Williams, Katerina Antypas
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • Cover; Half Title Page; Series Page; Title Page; Copyright Page; Contents; Foreword; Preface; About the Editors; Contributors; Chapter 1: Portable Methodologies for Energy Optimization on Large-Scale Power-Constrained Systems; 1.1 Introduction; 1.2 Background: How Architectures Drive the ASET Approach; 1.3 The ASET Approach; 1.3.1 Optimizing Per-Core Energy; 1.3.2 Optimizing Power Allocation across a Parallel System; 1.4 ASET Implementation; 1.4.1 Example: Wave-Front Algorithms; 1.4.2 Example: Load-Imbalanced Workloads; 1.5 Case Study: ASETs versus Dynamic Load Balancing
  • 1.5.1 Power Measurements and Analysis1.6 Conclusions; References; Chapter 2: Performance Analysis and Debugging Tools at Scale; 2.1 Introduction; 2.2 Tool and Debugger Building Blocks; 2.2.1 Hardware Performance Counters; 2.2.2 Sampling; 2.2.2.1 Event-Based Sampling; 2.2.2.2 Instruction-Based Sampling; 2.2.2.3 Data-Centric Sampling; 2.2.3 Call Stack Unwinding; 2.2.4 Instrumentation; 2.2.4.1 Source-Code Instrumentation; 2.2.4.2 Compiler-Based Instrumentation; 2.2.4.3 Binary Instrumentation; 2.2.5 Library Interposition; 2.2.6 Tracing; 2.2.7 GPU Performance Tools and Interfaces
  • 2.2.8 MPI Profiling, Tools, and Process Acquisition Interfaces2.2.9 OMPTâ#x80;#x94;A Performance Tool Interface for OpenMP; 2.2.10 Process Management Interfaceâ#x80;#x94;Exascale; 2.2.10.1 Architecture and Infrastructure; 2.2.10.2 Requirements; 2.3 Performance Tools; 2.3.1 Performance Application Programming Interface; 2.3.2 HPCToolkit; 2.3.3 TAU; 2.3.4 Score-P; 2.3.5 Vampir; 2.3.6 Darshan; 2.4 Debugging Tools; 2.4.1 Allinea DDT; 2.4.2 The TotalView Debugger; 2.4.2.1 Asynchronous Thread Control; 2.4.2.2 Reverse Debugging; 2.4.2.3 Heterogeneous Debugging; 2.4.2.4 Architecture and Infrastructure
  • 2.4.2.5 Multicast and Reduction2.4.2.6 Debugger Requirements; 2.4.3 Valgrind and Memory Debugging Tools; 2.4.4 Stack Trace Analysis Tool; 2.4.5 MPI and Thread Debugging; 2.5 Conclusions; References; Chapter 3: Exascale Challenges in Numerical Linear and Multilinear Algebras; 3.1 Introduction; 3.2 Linear Algebra; 3.2.1 Applications; 3.2.2 Linear Algebra Operations: State of Practice; 3.2.2.1 Dense Linear Algebra Operations; 3.2.2.2 Sparse Linear Algebra Operations; 3.2.3 Parallel and Accelerated Algorithms; 3.2.3.1 Hardware Considerations; 3.2.3.2 Dense Linear Algebra Algorithms
  • 3.2.3.3 Sparse Linear Algebra Algorithms3.2.4 Extreme Scale Issues; 3.2.4.1 Higher Thread Count; 3.2.4.2 Changing Memory Hierarchies; 3.2.4.3 Communication Network Developments; 3.2.4.4 Growing Resilience Concerns; 3.2.5 Software; 3.2.5.1 Third-Party Libraries; 3.2.5.2 Vendor Libraries; 3.2.6 Conclusion; 3.3 Tensor Algebra; 3.3.1 Tensors in Different Scientific Disciplines; 3.3.2 Basic Tensor Algebra Operations; 3.3.3 Tensor Decompositions and Higher Level Operations; 3.3.4 Parallel Algorithms for Basic Tensor Operations; 3.3.5 Extreme Scale Solutions
Control code
on1011673656
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781315277400
Level of compression
unknown
Note
Taylor & Francis
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1011673656
Label
Exascale scientific applications : scalability and performance portability, edited by Tjerk P. Straatsma, Timothy J. Williams, Katerina Antypas
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • Cover; Half Title Page; Series Page; Title Page; Copyright Page; Contents; Foreword; Preface; About the Editors; Contributors; Chapter 1: Portable Methodologies for Energy Optimization on Large-Scale Power-Constrained Systems; 1.1 Introduction; 1.2 Background: How Architectures Drive the ASET Approach; 1.3 The ASET Approach; 1.3.1 Optimizing Per-Core Energy; 1.3.2 Optimizing Power Allocation across a Parallel System; 1.4 ASET Implementation; 1.4.1 Example: Wave-Front Algorithms; 1.4.2 Example: Load-Imbalanced Workloads; 1.5 Case Study: ASETs versus Dynamic Load Balancing
  • 1.5.1 Power Measurements and Analysis1.6 Conclusions; References; Chapter 2: Performance Analysis and Debugging Tools at Scale; 2.1 Introduction; 2.2 Tool and Debugger Building Blocks; 2.2.1 Hardware Performance Counters; 2.2.2 Sampling; 2.2.2.1 Event-Based Sampling; 2.2.2.2 Instruction-Based Sampling; 2.2.2.3 Data-Centric Sampling; 2.2.3 Call Stack Unwinding; 2.2.4 Instrumentation; 2.2.4.1 Source-Code Instrumentation; 2.2.4.2 Compiler-Based Instrumentation; 2.2.4.3 Binary Instrumentation; 2.2.5 Library Interposition; 2.2.6 Tracing; 2.2.7 GPU Performance Tools and Interfaces
  • 2.2.8 MPI Profiling, Tools, and Process Acquisition Interfaces2.2.9 OMPTâ#x80;#x94;A Performance Tool Interface for OpenMP; 2.2.10 Process Management Interfaceâ#x80;#x94;Exascale; 2.2.10.1 Architecture and Infrastructure; 2.2.10.2 Requirements; 2.3 Performance Tools; 2.3.1 Performance Application Programming Interface; 2.3.2 HPCToolkit; 2.3.3 TAU; 2.3.4 Score-P; 2.3.5 Vampir; 2.3.6 Darshan; 2.4 Debugging Tools; 2.4.1 Allinea DDT; 2.4.2 The TotalView Debugger; 2.4.2.1 Asynchronous Thread Control; 2.4.2.2 Reverse Debugging; 2.4.2.3 Heterogeneous Debugging; 2.4.2.4 Architecture and Infrastructure
  • 2.4.2.5 Multicast and Reduction2.4.2.6 Debugger Requirements; 2.4.3 Valgrind and Memory Debugging Tools; 2.4.4 Stack Trace Analysis Tool; 2.4.5 MPI and Thread Debugging; 2.5 Conclusions; References; Chapter 3: Exascale Challenges in Numerical Linear and Multilinear Algebras; 3.1 Introduction; 3.2 Linear Algebra; 3.2.1 Applications; 3.2.2 Linear Algebra Operations: State of Practice; 3.2.2.1 Dense Linear Algebra Operations; 3.2.2.2 Sparse Linear Algebra Operations; 3.2.3 Parallel and Accelerated Algorithms; 3.2.3.1 Hardware Considerations; 3.2.3.2 Dense Linear Algebra Algorithms
  • 3.2.3.3 Sparse Linear Algebra Algorithms3.2.4 Extreme Scale Issues; 3.2.4.1 Higher Thread Count; 3.2.4.2 Changing Memory Hierarchies; 3.2.4.3 Communication Network Developments; 3.2.4.4 Growing Resilience Concerns; 3.2.5 Software; 3.2.5.1 Third-Party Libraries; 3.2.5.2 Vendor Libraries; 3.2.6 Conclusion; 3.3 Tensor Algebra; 3.3.1 Tensors in Different Scientific Disciplines; 3.3.2 Basic Tensor Algebra Operations; 3.3.3 Tensor Decompositions and Higher Level Operations; 3.3.4 Parallel Algorithms for Basic Tensor Operations; 3.3.5 Extreme Scale Solutions
Control code
on1011673656
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781315277400
Level of compression
unknown
Note
Taylor & Francis
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1011673656

Library Locations

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