High-Performance Computing on Complex Environments

1 - Cover [Seite 1]
2 - Title Page [Seite 5]
3 - Contents [Seite 9]
4 - Contributors [Seite 25]
5 - Preface [Seite 29]
6 - European Science Foundation [Seite 31]
7 - Part I Introduction [Seite 33]
7.1 - Chapter 1 Summary of the Open European Network for High-Performance Computing in Complex Environments [Seite 35]
7.1.1 - 1.1 Introduction and Vision [Seite 36]
7.1.2 - 1.2 Scientific Organization [Seite 38]
7.1.2.1 - 1.2.1 Scientific Focus [Seite 38]
7.1.2.2 - 1.2.2 Working Groups [Seite 38]
7.1.3 - 1.3 Activities of the Project [Seite 38]
7.1.3.1 - 1.3.1 Spring Schools [Seite 38]
7.1.3.2 - 1.3.2 International Workshops [Seite 39]
7.1.3.3 - 1.3.3 Working Groups Meetings [Seite 39]
7.1.3.4 - 1.3.4 Management Committee Meetings [Seite 39]
7.1.3.5 - 1.3.5 Short-Term Scientific Missions [Seite 39]
7.1.4 - 1.4 Main Outcomes of the Action [Seite 39]
7.1.5 - 1.5 Contents of the Book [Seite 40]
7.1.6 - Acknowledgment [Seite 42]
8 - Part II Numerical Analysis for Heterogeneous and Multicore Systems [Seite 43]
8.1 - Chapter 2 On the Impact of the Heterogeneous Multicore and Many-Core Platforms on Iterative Solution Methods and Preconditioning Techniques [Seite 45]
8.1.1 - 2.1 Introduction [Seite 46]
8.1.2 - 2.2 General Description of Iterative Methods and Preconditioning [Seite 48]
8.1.2.1 - 2.2.1 Basic Iterative Methods [Seite 48]
8.1.2.2 - 2.2.2 Projection Methods: CG and GMRES [Seite 50]
8.1.3 - 2.3 Preconditioning Techniques [Seite 52]
8.1.4 - 2.4 Defect-Correction Technique [Seite 53]
8.1.5 - 2.5 Multigrid Method [Seite 54]
8.1.6 - 2.6 Parallelization of Iterative Methods [Seite 54]
8.1.7 - 2.7 Heterogeneous Systems [Seite 55]
8.1.7.1 - 2.7.1 Heterogeneous Computing [Seite 56]
8.1.7.2 - 2.7.2 Algorithm Characteristics and Resource Utilization [Seite 57]
8.1.7.3 - 2.7.3 Exposing Parallelism [Seite 58]
8.1.7.4 - 2.7.4 Heterogeneity in Matrix Computation [Seite 58]
8.1.7.5 - 2.7.5 Setup of Heterogeneous Iterative Solvers [Seite 59]
8.1.8 - 2.8 Maintenance and Portability [Seite 61]
8.1.9 - 2.9 Conclusion [Seite 62]
8.1.10 - Acknowledgments [Seite 63]
8.1.11 - References [Seite 63]
8.2 - Chapter 3 Efficient Numerical Solution of 2D Diffusion Equation on Multicore Computers [Seite 65]
8.2.1 - 3.1 Introduction [Seite 66]
8.2.2 - 3.2 Test Case [Seite 67]
8.2.2.1 - 3.2.1 Governing Equations [Seite 67]
8.2.2.2 - 3.2.2 Solution Procedure [Seite 68]
8.2.3 - 3.3 Parallel Implementation [Seite 71]
8.2.3.1 - 3.3.1 Intel PCM Library [Seite 71]
8.2.3.2 - 3.3.2 OpenMP [Seite 72]
8.2.4 - 3.4 Results [Seite 73]
8.2.4.1 - 3.4.1 Results of Numerical Integration [Seite 73]
8.2.4.2 - 3.4.2 Parallel Efficiency [Seite 74]
8.2.5 - 3.5 Discussion [Seite 77]
8.2.6 - 3.6 Conclusion [Seite 79]
8.2.7 - Acknowledgment [Seite 79]
8.2.8 - References [Seite 79]
8.3 - Chapter 4 Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience [Seite 83]
8.3.1 - 4.1 Introduction [Seite 83]
8.3.2 - 4.2 Formulation of the Discrete Model [Seite 85]
8.3.2.1 - 4.2.1 The theta-Implicit Discrete Scheme [Seite 87]
8.3.2.2 - 4.2.2 The Predictor--Corrector Algorithm I [Seite 89]
8.3.2.3 - 4.2.3 The Predictor--Corrector Algorithm II [Seite 90]
8.3.3 - 4.3 Parallel Algorithms [Seite 91]
8.3.3.1 - 4.3.1 Parallel theta-Implicit Algorithm [Seite 91]
8.3.3.2 - 4.3.2 Parallel Predictor--Corrector Algorithm I [Seite 94]
8.3.3.3 - 4.3.3 Parallel Predictor--Corrector Algorithm II [Seite 95]
8.3.4 - 4.4 Computational Results [Seite 95]
8.3.4.1 - 4.4.1 Experimental Comparison of Predictor--Corrector Algorithms [Seite 98]
8.3.4.2 - 4.4.2 Numerical Experiment of Neuron Excitation [Seite 100]
8.3.5 - 4.5 Conclusions [Seite 101]
8.3.6 - Acknowledgments [Seite 102]
8.3.7 - References [Seite 102]
9 - Part III Communication and Storage Considerations in High-Performance Computing [Seite 105]
9.1 - Chapter 5 An Overview of Topology Mapping Algorithms and Techniques in High-Performance Computing [Seite 107]
9.1.1 - 5.1 Introduction [Seite 108]
9.1.2 - 5.2 General Overview [Seite 108]
9.1.2.1 - 5.2.1 A Key to Scalability: Data Locality [Seite 109]
9.1.2.2 - 5.2.2 Data Locality Management in Parallel Programming Models [Seite 109]
9.1.2.3 - 5.2.3 Virtual Topology: Definition and Characteristics [Seite 110]
9.1.2.4 - 5.2.4 Understanding the Hardware [Seite 111]
9.1.3 - 5.3 Formalization of the Problem [Seite 111]
9.1.4 - 5.4 Algorithmic Strategies for Topology Mapping [Seite 113]
9.1.4.1 - 5.4.1 Greedy Algorithm Variants [Seite 113]
9.1.4.2 - 5.4.2 Graph Partitioning [Seite 114]
9.1.4.3 - 5.4.3 Schemes Based on Graph Similarity [Seite 114]
9.1.4.4 - 5.4.4 Schemes Based on Subgraph Isomorphism [Seite 114]
9.1.5 - 5.5 Mapping Enforcement Techniques [Seite 114]
9.1.5.1 - 5.5.1 Resource Binding [Seite 115]
9.1.5.2 - 5.5.2 Rank Reordering [Seite 115]
9.1.5.3 - 5.5.3 Other Techniques [Seite 116]
9.1.6 - 5.6 Survey of Solutions [Seite 117]
9.1.6.1 - 5.6.1 Algorithmic Solutions [Seite 117]
9.1.6.2 - 5.6.2 Existing Implementations [Seite 117]
9.1.7 - 5.7 Conclusion and Open Problems [Seite 121]
9.1.8 - Acknowledgment [Seite 122]
9.1.9 - References [Seite 122]
9.2 - Chapter 6 Optimization of Collective Communication for Heterogeneous HPC Platforms [Seite 127]
9.2.1 - 6.1 Introduction [Seite 127]
9.2.2 - 6.2 Overview of Optimized Collectives and Topology-Aware Collectives [Seite 129]
9.2.3 - 6.3 Optimizations of Collectives on Homogeneous Clusters [Seite 130]
9.2.4 - 6.4 Heterogeneous Networks [Seite 131]
9.2.4.1 - 6.4.1 Comparison to Homogeneous Clusters [Seite 131]
9.2.5 - 6.5 Topology- and Performance-Aware Collectives [Seite 132]
9.2.6 - 6.6 Topology as Input [Seite 133]
9.2.7 - 6.7 Performance as Input [Seite 134]
9.2.7.1 - 6.7.1 Homogeneous Performance Models [Seite 135]
9.2.7.2 - 6.7.2 Heterogeneous Performance Models [Seite 137]
9.2.7.3 - 6.7.3 Estimation of Parameters of Heterogeneous Performance Models [Seite 138]
9.2.7.4 - 6.7.4 Other Performance Models [Seite 138]
9.2.8 - 6.8 Non-MPI Collective Algorithms for Heterogeneous Networks [Seite 138]
9.2.8.1 - 6.8.1 Optimal Solutions with Multiple Spanning Trees [Seite 139]
9.2.8.2 - 6.8.2 Adaptive Algorithms for Efficient Large-Message Transfer [Seite 139]
9.2.8.3 - 6.8.3 Network Models Inspired by BitTorrent [Seite 140]
9.2.9 - 6.9 Conclusion [Seite 143]
9.2.10 - Acknowledgments [Seite 143]
9.2.11 - References [Seite 143]
9.3 - Chapter 7 Effective Data Access Patterns on Massively Parallel Processors [Seite 147]
9.3.1 - 7.1 Introduction [Seite 147]
9.3.2 - 7.2 Architectural Details [Seite 148]
9.3.3 - 7.3 K-Model [Seite 149]
9.3.3.1 - 7.3.1 The Architecture [Seite 149]
9.3.3.2 - 7.3.2 Cost and Complexity Evaluation [Seite 150]
9.3.3.3 - 7.3.3 Efficiency Evaluation [Seite 151]
9.3.4 - 7.4 Parallel Prefix Sum [Seite 152]
9.3.4.1 - 7.4.1 Experiments [Seite 157]
9.3.5 - 7.5 Bitonic Sorting Networks [Seite 158]
9.3.5.1 - 7.5.1 Experiments [Seite 163]
9.3.6 - 7.6 Final Remarks [Seite 164]
9.3.7 - Acknowledgments [Seite 165]
9.3.8 - References [Seite 165]
9.4 - Chapter 8 Scalable Storage I/O Software for Blue Gene Architectures [Seite 167]
9.4.1 - 8.1 Introduction [Seite 167]
9.4.2 - 8.2 Blue Gene System Overview [Seite 168]
9.4.2.1 - 8.2.1 Blue Gene Architecture [Seite 168]
9.4.2.2 - 8.2.2 Operating System Architecture [Seite 168]
9.4.3 - 8.3 Design and Implementation [Seite 170]
9.4.3.1 - 8.3.1 The Client Module [Seite 171]
9.4.3.2 - 8.3.2 The I/O Module [Seite 173]
9.4.4 - 8.4 Conclusions and Future Work [Seite 174]
9.4.5 - Acknowledgments [Seite 174]
9.4.6 - References [Seite 174]
10 - Part IV Efficient Exploitation of Heterogeneous Architectures [Seite 177]
10.1 - Chapter 9 Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems [Seite 179]
10.1.1 - 9.1 Introduction [Seite 180]
10.1.1.1 - 9.1.1 Application Model [Seite 180]
10.1.1.2 - 9.1.2 System Model [Seite 183]
10.1.1.3 - 9.1.3 Performance Metrics [Seite 184]
10.1.2 - 9.2 Concurrent Workflow Scheduling [Seite 185]
10.1.2.1 - 9.2.1 Offline Scheduling of Concurrent Workflows [Seite 186]
10.1.2.2 - 9.2.2 Online Scheduling of Concurrent Workflows [Seite 187]
10.1.3 - 9.3 Experimental Results and Discussion [Seite 192]
10.1.3.1 - 9.3.1 DAG Structure [Seite 192]
10.1.3.2 - 9.3.2 Simulated Platforms [Seite 192]
10.1.3.3 - 9.3.3 Results and Discussion [Seite 194]
10.1.4 - 9.4 Conclusions [Seite 197]
10.1.5 - Acknowledgments [Seite 198]
10.1.6 - References [Seite 198]
10.2 - Chapter 10 Systematic Mapping of Reed--Solomon Erasure Codes on Heterogeneous Multicore Architectures [Seite 201]
10.2.1 - 10.1 Introduction [Seite 201]
10.2.2 - 10.2 Related Works [Seite 203]
10.2.3 - 10.3 Reed--Solomon Codes and Linear Algebra Algorithms [Seite 204]
10.2.4 - 10.4 Mapping Reed--Solomon Codes on Cell/B.E. Architecture [Seite 205]
10.2.4.1 - 10.4.1 Cell/B.E. Architecture [Seite 205]
10.2.4.2 - 10.4.2 Basic Assumptions for Mapping [Seite 206]
10.2.4.3 - 10.4.3 Vectorization Algorithm and Increasing its Efficiency [Seite 207]
10.2.4.4 - 10.4.4 Performance Results [Seite 209]
10.2.5 - 10.5 Mapping Reed--Solomon Codes on Multicore GPU Architectures [Seite 210]
10.2.5.1 - 10.5.1 Parallelization of Reed--Solomon Codes on GPU Architectures [Seite 210]
10.2.5.2 - 10.5.2 Organization of GPU Threads [Seite 212]
10.2.6 - 10.6 Methods of Increasing the Algorithm Performance on GPUs [Seite 213]
10.2.6.1 - 10.6.1 Basic Modifications [Seite 213]
10.2.6.2 - 10.6.2 Stream Processing [Seite 214]
10.2.6.3 - 10.6.3 Using Shared Memory [Seite 216]
10.2.7 - 10.7 GPU Performance Evaluation [Seite 217]
10.2.7.1 - 10.7.1 Experimental Results [Seite 217]
10.2.7.2 - 10.7.2 Performance Analysis using the Roofline Model [Seite 219]
10.2.8 - 10.8 Conclusions and Future Works [Seite 222]
10.2.9 - Acknowledgments [Seite 223]
10.2.10 - References [Seite 223]
10.3 - Chapter 11 Heterogeneous Parallel Computing Platforms and Tools for Compute-Intensive Algorithms: A Case Study [Seite 225]
10.3.1 - 11.1 Introduction [Seite 226]
10.3.2 - 11.2 A Low-Cost Heterogeneous Computing Environment [Seite 228]
10.3.2.1 - 11.2.1 Adopted Computing Environment [Seite 231]
10.3.3 - 11.3 First Case Study: The N-Body Problem [Seite 232]
10.3.3.1 - 11.3.1 The Sequential N-Body Algorithm [Seite 233]
10.3.3.2 - 11.3.2 The Parallel N-Body Algorithm for Multicore Architectures [Seite 235]
10.3.3.3 - 11.3.3 The Parallel N-Body Algorithm for CUDA Architectures [Seite 236]
10.3.4 - 11.4 Second Case Study: The Convolution Algorithm [Seite 238]
10.3.4.1 - 11.4.1 The Sequential Convolver Algorithm [Seite 238]
10.3.4.2 - 11.4.2 The Parallel Convolver Algorithm for Multicore Architectures [Seite 239]
10.3.4.3 - 11.4.3 The Parallel Convolver Algorithm for GPU Architectures [Seite 240]
10.3.5 - 11.5 Conclusions [Seite 243]
10.3.6 - Acknowledgments [Seite 244]
10.3.7 - References [Seite 244]
10.4 - Chapter 12 Efficient Application of Hybrid Parallelism in Electromagnetism Problems [Seite 247]
10.4.1 - 12.1 Introduction [Seite 247]
10.4.2 - 12.2 Computation of Green's functions in Hybrid Systems [Seite 248]
10.4.2.1 - 12.2.1 Computation in a Heterogeneous Cluster [Seite 249]
10.4.2.2 - 12.2.2 Experiments [Seite 250]
10.4.3 - 12.3 Parallelization in Numa Systems of a Volume Integral Equation Technique [Seite 254]
10.4.3.1 - 12.3.1 Experiments [Seite 254]
10.4.4 - 12.4 Autotuning Parallel Codes [Seite 258]
10.4.4.1 - 12.4.1 Empirical Autotuning [Seite 259]
10.4.4.2 - 12.4.2 Modeling the Linear Algebra Routines [Seite 261]
10.4.5 - 12.5 Conclusions and Future Research [Seite 262]
10.4.6 - Acknowledgments [Seite 263]
10.4.7 - References [Seite 264]
11 - Part V CPU + GPU Coprocessing [Seite 267]
11.1 - Chapter 13 Design and Optimization of Scientific Applications for Highly Heterogeneous and Hierarchical HPC Platforms Using Functional Computation Performance Models [Seite 269]
11.1.1 - 13.1 Introduction [Seite 270]
11.1.2 - 13.2 Related Work [Seite 273]
11.1.3 - 13.3 Data Partitioning Based on Functional Performance Model [Seite 275]
11.1.4 - 13.4 Example Application: Heterogeneous Parallel Matrix Multiplication [Seite 277]
11.1.5 - 13.5 Performance Measurement on CPUs/GPUs System [Seite 279]
11.1.6 - 13.6 Functional Performance Models of Multiple Cores and GPUs [Seite 280]
11.1.7 - 13.7 FPM-Based Data Partitioning on CPUs/GPUs System [Seite 282]
11.1.8 - 13.8 Efficient Building of Functional Performance Models [Seite 283]
11.1.9 - 13.9 FPM-Based Data Partitioning on Hierarchical Platforms [Seite 285]
11.1.10 - 13.10 Conclusion [Seite 289]
11.1.11 - Acknowledgments [Seite 291]
11.1.12 - References [Seite 291]
11.2 - Chapter 14 Efficient Multilevel Load Balancing on Heterogeneous CPU + GPU Systems [Seite 293]
11.2.1 - 14.1 Introduction: Heterogeneous CPU + GPU Systems [Seite 294]
11.2.1.1 - 14.1.1 Open Problems and Specific Contributions [Seite 295]
11.2.2 - 14.2 Background and Related Work [Seite 297]
11.2.2.1 - 14.2.1 Divisible Load Scheduling in Distributed CPU-Only Systems [Seite 297]
11.2.2.2 - 14.2.2 Scheduling in Multicore CPU and Multi-GPU Environments [Seite 300]
11.2.3 - 14.3 Load Balancing Algorithms for Heterogeneous CPU + GPU Systems [Seite 301]
11.2.3.1 - 14.3.1 Multilevel Simultaneous Load Balancing Algorithm [Seite 302]
11.2.3.2 - 14.3.2 Algorithm for Multi-Installment Processing with Multidistributions [Seite 305]
11.2.4 - 14.4 Experimental Results [Seite 307]
11.2.4.1 - 14.4.1 MSLBA Evaluation: Dense Matrix Multiplication Case Study [Seite 307]
11.2.4.2 - 14.4.2 AMPMD Evaluation: 2D FFT Case Study [Seite 309]
11.2.5 - 14.5 Conclusions [Seite 311]
11.2.6 - Acknowledgments [Seite 312]
11.2.7 - References [Seite 312]
11.3 - Chapter 15 The All-Pair Shortest-Path Problem in Shared-Memory Heterogeneous Systems [Seite 315]
11.3.1 - 15.1 Introduction [Seite 315]
11.3.2 - 15.2 Algorithmic Overview [Seite 317]
11.3.2.1 - 15.2.1 Graph Theory Notation [Seite 317]
11.3.2.2 - 15.2.2 Dijkstra's Algorithm [Seite 318]
11.3.2.3 - 15.2.3 Parallel Version of Dijkstra's Algorithm [Seite 319]
11.3.3 - 15.3 CUDA Overview [Seite 319]
11.3.4 - 15.4 Heterogeneous Systems and Load Balancing [Seite 320]
11.3.5 - 15.5 Parallel Solutions to The APSP [Seite 321]
11.3.5.1 - 15.5.1 GPU Implementation [Seite 321]
11.3.5.2 - 15.5.2 Heterogeneous Implementation [Seite 322]
11.3.6 - 15.6 Experimental Setup [Seite 323]
11.3.6.1 - 15.6.1 Methodology [Seite 323]
11.3.6.2 - 15.6.2 Target Architectures [Seite 324]
11.3.6.3 - 15.6.3 Input Set Characteristics [Seite 324]
11.3.6.4 - 15.6.4 Load-Balancing Techniques Evaluated [Seite 324]
11.3.7 - 15.7 Experimental Results [Seite 325]
11.3.7.1 - 15.7.1 Complete APSP [Seite 325]
11.3.7.2 - 15.7.2 512-Source-Node-to-All Shortest Path [Seite 327]
11.3.7.3 - 15.7.3 Experimental Conclusions [Seite 328]
11.3.8 - 15.8 Conclusions [Seite 329]
11.3.9 - Acknowledgments [Seite 329]
11.3.10 - References [Seite 329]
12 - Part VI Efficient Exploitation of Distributed Systems [Seite 333]
12.1 - Chapter 16 Resource Management for HPC on the Cloud [Seite 335]
12.1.1 - 16.1 Introduction [Seite 335]
12.1.2 - 16.2 On the Type of Applications for HPC and HPC2 [Seite 337]
12.1.3 - 16.3 HPC on the Cloud [Seite 338]
12.1.3.1 - 16.3.1 General PaaS Solutions [Seite 338]
12.1.3.2 - 16.3.2 On-Demand Platforms for HPC [Seite 342]
12.1.4 - 16.4 Scheduling Algorithms for HPC2 [Seite 343]
12.1.5 - 16.5 Toward an Autonomous Scheduling Framework [Seite 344]
12.1.5.1 - 16.5.1 Autonomous Framework for RMS [Seite 345]
12.1.5.2 - 16.5.2 Self-Management [Seite 347]
12.1.5.3 - 16.5.3 Use Cases [Seite 349]
12.1.6 - 16.6 Conclusions [Seite 351]
12.1.7 - Acknowledgment [Seite 352]
12.1.8 - References [Seite 352]
12.2 - Chapter 17 Resource Discovery in Large-Scale Grid Systems [Seite 355]
12.2.1 - 17.1 Introduction and Background [Seite 355]
12.2.1.1 - 17.1.1 Introduction [Seite 355]
12.2.1.2 - 17.1.2 Resource Discovery in Grids [Seite 356]
12.2.1.3 - 17.1.3 Background [Seite 357]
12.2.2 - 17.2 The Semantic Communities Approach [Seite 357]
12.2.2.1 - 17.2.1 Grid Resource Discovery Using Semantic Communities [Seite 357]
12.2.2.2 - 17.2.2 Grid Resource Discovery Based on Semantically Linked Virtual Organizations [Seite 359]
12.2.3 - 17.3 The P2P Approach [Seite 361]
12.2.3.1 - 17.3.1 On Fully Decentralized Resource Discovery in Grid Environments Using a P2P Architecture [Seite 361]
12.2.3.2 - 17.3.2 P2P Protocols for Resource Discovery in the Grid [Seite 362]
12.2.4 - 17.4 The Grid-Routing Transferring Approach [Seite 365]
12.2.4.1 - 17.4.1 Resource Discovery Based on Matchmaking Routers [Seite 365]
12.2.4.2 - 17.4.2 Acquiring Knowledge in a Large-Scale Grid System [Seite 367]
12.2.5 - 17.5 Conclusions [Seite 369]
12.2.6 - Acknowledgment [Seite 370]
12.2.7 - References [Seite 370]
13 - Part VII Energy Awareness in High-Performance Computing [Seite 373]
13.1 - Chapter 18 Energy-Aware Approaches for HPC Systems [Seite 375]
13.1.1 - 18.1 Introduction [Seite 376]
13.1.2 - 18.2 Power Consumption of Servers [Seite 377]
13.1.2.1 - 18.2.1 Server Modeling [Seite 378]
13.1.2.2 - 18.2.2 Power Prediction Models [Seite 379]
13.1.3 - 18.3 Classification and Energy Profiles of HPC Applications [Seite 386]
13.1.3.1 - 18.3.1 Phase Detection [Seite 388]
13.1.3.2 - 18.3.2 Phase Identification [Seite 390]
13.1.4 - 18.4 Policies and Leverages [Seite 391]
13.1.5 - 18.5 Conclusion [Seite 392]
13.1.6 - Acknowledgements [Seite 393]
13.1.7 - References [Seite 393]
13.2 - Chapter 19 Strategies for Increased Energy Awareness in Cloud Federations [Seite 397]
13.2.1 - 19.1 Introduction [Seite 397]
13.2.2 - 19.2 Related Work [Seite 399]
13.2.3 - 19.3 Scenarios [Seite 401]
13.2.3.1 - 19.3.1 Increased Energy Awareness Across Multiple Data Centers within a Single Administrative Domain [Seite 401]
13.2.3.2 - 19.3.2 Energy Considerations in Commercial Cloud Federations [Seite 404]
13.2.3.3 - 19.3.3 Reduced Energy Footprint of Academic Cloud Federations [Seite 406]
13.2.4 - 19.4 Energy-Aware Cloud Federations [Seite 406]
13.2.4.1 - 19.4.1 Availability of Energy-Consumption-Related Information [Seite 407]
13.2.4.2 - 19.4.2 Service Call Scheduling at the Meta-Brokering Level of FCM [Seite 408]
13.2.4.3 - 19.4.3 Service Call Scheduling and VM Management at the Cloud-Brokering Level of FCM [Seite 409]
13.2.5 - 19.5 Conclusions [Seite 411]
13.2.6 - Acknowledgments [Seite 412]
13.2.7 - References [Seite 412]
13.3 - Chapter 20 Enabling Network Security in HPC Systems Using Heterogeneous CMPs [Seite 415]
13.3.1 - 20.1 Introduction [Seite 416]
13.3.2 - 20.2 Related Work [Seite 418]
13.3.3 - 20.3 Overview of Our Approach [Seite 419]
13.3.3.1 - 20.3.1 Heterogeneous CMP Architecture [Seite 419]
13.3.3.2 - 20.3.2 Network Security Application Behavior [Seite 420]
13.3.3.3 - 20.3.3 High-Level View [Seite 421]
13.3.4 - 20.4 Heterogeneous CMP Design for Network Security Processors [Seite 422]
13.3.4.1 - 20.4.1 Task Assignment [Seite 422]
13.3.4.2 - 20.4.2 ILP Formulation [Seite 423]
13.3.4.3 - 20.4.3 Discussion [Seite 425]
13.3.5 - 20.5 Experimental Evaluation [Seite 426]
13.3.5.1 - 20.5.1 Setup [Seite 426]
13.3.5.2 - 20.5.2 Results [Seite 427]
13.3.6 - 20.6 Concluding Remarks [Seite 429]
13.3.7 - Acknowledgments [Seite 429]
13.3.8 - References [Seite 429]
14 - Part VIII Applications of Heterogeneous High-Performance Computing [Seite 433]
14.1 - Chapter 21 Toward a High-Performance Distributed CBIR System for Hyperspectral Remote Sensing Data: A Case Study in Jungle Computing [Seite 435]
14.1.1 - 21.1 Introduction [Seite 436]
14.1.2 - 21.2 CBIR For Hyperspectral Imaging Data [Seite 439]
14.1.2.1 - 21.2.1 Spectral Unmixing [Seite 439]
14.1.2.2 - 21.2.2 Proposed CBIR System [Seite 441]
14.1.3 - 21.3 Jungle Computing [Seite 442]
14.1.3.1 - 21.3.1 Jungle Computing: Requirements [Seite 443]
14.1.4 - 21.4 IBIS and Constellation [Seite 444]
14.1.5 - 21.5 System Design and Implementation [Seite 447]
14.1.5.1 - 21.5.1 Endmember Extraction [Seite 450]
14.1.5.2 - 21.5.2 Query Execution [Seite 450]
14.1.5.3 - 21.5.3 Equi-Kernels [Seite 451]
14.1.5.4 - 21.5.4 Matchmaking [Seite 452]
14.1.6 - 21.6 Evaluation [Seite 452]
14.1.6.1 - 21.6.1 Performance Evaluation [Seite 453]
14.1.7 - 21.7 Conclusions [Seite 458]
14.1.8 - Acknowledgments [Seite 458]
14.1.9 - References [Seite 458]
14.2 - Chapter 22 Taking Advantage of Heterogeneous Platforms in Image and Video Processing [Seite 461]
14.2.1 - 22.1 Introduction [Seite 462]
14.2.2 - 22.2 Related Work [Seite 463]
14.2.2.1 - 22.2.1 Image Processing on GPU [Seite 463]
14.2.2.2 - 22.2.2 Video Processing on GPU [Seite 464]
14.2.2.3 - 22.2.3 Contribution [Seite 465]
14.2.3 - 22.3 Parallel Image Processing on GPU [Seite 465]
14.2.3.1 - 22.3.1 Development Scheme for Image Processing on GPU [Seite 465]
14.2.3.2 - 22.3.2 GPU Optimization [Seite 466]
14.2.3.3 - 22.3.3 GPU Implementation of Edge and Corner Detection [Seite 466]
14.2.3.4 - 22.3.4 Performance Analysis and Evaluation [Seite 466]
14.2.4 - 22.4 Image Processing on Heterogeneous Architectures [Seite 469]
14.2.4.1 - 22.4.1 Development Scheme for Multiple Image Processing [Seite 469]
14.2.4.2 - 22.4.2 Task Scheduling within Heterogeneous Architectures [Seite 470]
14.2.4.3 - 22.4.3 Optimization Within Heterogeneous Architectures [Seite 470]
14.2.5 - 22.5 Video Processing on GPU [Seite 470]
14.2.5.1 - 22.5.1 Development Scheme for Video Processing on GPU [Seite 471]
14.2.5.2 - 22.5.2 GPU Optimizations [Seite 472]
14.2.5.3 - 22.5.3 GPU Implementations [Seite 472]
14.2.5.4 - 22.5.4 GPU-Based Silhouette Extraction [Seite 472]
14.2.5.5 - 22.5.5 GPU-Based Optical Flow Estimation [Seite 472]
14.2.5.6 - 22.5.6 Result Analysis [Seite 475]
14.2.6 - 22.6 Experimental Results [Seite 476]
14.2.6.1 - 22.6.1 Heterogeneous Computing for Vertebra Segmentation [Seite 476]
14.2.6.2 - 22.6.2 GPU Computing for Motion Detection Using a Moving Camera [Seite 477]
14.2.7 - 22.7 Conclusion [Seite 479]
14.2.8 - Acknowledgment [Seite 480]
14.2.9 - References [Seite 480]
14.3 - Chapter 23 Real-Time Tomographic Reconstruction Through CPU + GPU Coprocessing [Seite 483]
14.3.1 - 23.1 Introduction [Seite 484]
14.3.2 - 23.2 Tomographic Reconstruction [Seite 485]
14.3.3 - 23.3 Optimization of Tomographic Reconstruction for CPUs and for GPUs [Seite 487]
14.3.4 - 23.4 Hybrid CPU + GPU Tomographic Reconstruction [Seite 489]
14.3.5 - 23.5 Results [Seite 491]
14.3.6 - 23.6 Discussion and Conclusion [Seite 493]
14.3.7 - Acknowledgments [Seite 495]
14.3.8 - References [Seite 495]
15 - Index [Seite 499]
16 - Series Page [Seite 501]