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High-Performance Computing on Complex Environments

ISBN: 978-1-118-71207-8
512 pages
April 2014, Wiley-IEEE Computer Society Press
High-Performance Computing on Complex Environments (1118712072) cover image

Description

With recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. It is important to provide a comprehensive study on how to use such machines written by specialists of the domain. The 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. The content spans topics such as 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.

• Covers cutting-edge research in HPC on complex environments, following an international collaboration of members of the ComplexHPC

• Explains how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems

• 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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Table of Contents

Contributors xxiii

Preface xxvii

PART I INTRODUCTION 1

1. Summary of the Open European Network for High-Performance Computing in Complex Environments 3
Emmanuel Jeannot and Julius Zilinskas

1.1 Introduction and Vision 4

1.2 Scientific Organization 6

1.3 Activities of the Project 6

1.4 Main Outcomes of the Action 7

1.5 Contents of the Book 8

PART II NUMERICAL ANALYSIS FOR HETEROGENEOUS AND MULTICORE SYSTEMS 11

2. On the Impact of the Heterogeneous Multicore and Many-Core Platforms on Iterative Solution Methods and Preconditioning Techniques 13
Dimitar Lukarski and Maya Neytcheva

2.1 Introduction 14

2.2 General Description of Iterative Methods and Preconditioning 16

2.3 Preconditioning Techniques 20

2.4 Defect-Correction Technique 21

2.5 Multigrid Method 22

2.6 Parallelization of Iterative Methods 22

2.7 Heterogeneous Systems 23

2.8 Maintenance and Portability 29

2.9 Conclusion 30

3. Efficient Numerical Solution of 2D Diffusion Equation on Multicore Computers 33
Matjaz Depolli, Gregor Kosec, and Roman Trobec

3.1 Introduction 34

3.2 Test Case 35

3.3 Parallel Implementation 39

3.4 Results 41

3.5 Discussion 45

3.6 Conclusion 47

4. Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience 51
Natalija Tumanova and Raimondas Ciegis

4.1 Introduction 51

4.2 Formulation of the Discrete Model 53

4.3 Parallel Algorithms 59

4.4 Computational Results 63

4.5 Conclusions 69

PART III COMMUNICATION AND STORAGE CONSIDERATIONS IN HIGH-PERFORMANCE COMPUTING 73

5. An Overview of Topology Mapping Algorithms and Techniques in High-Performance Computing 75
Torsten Hoefler, Emmanuel Jeannot, and Guillaume Mercier

5.1 Introduction 76

5.2 General Overview 76

5.3 Formalization of the Problem 79

5.4 Algorithmic Strategies for Topology Mapping 81

5.5 Mapping Enforcement Techniques 82

5.6 Survey of Solutions 85

5.7 Conclusion and Open Problems 89

6. Optimization of Collective Communication for Heterogeneous HPC Platforms 95
Kiril Dichev and Alexey Lastovetsky

6.1 Introduction 95

6.2 Overview of Optimized Collectives and Topology-Aware Collectives 97

6.3 Optimizations of Collectives on Homogeneous Clusters 98

6.4 Heterogeneous Networks 99

6.5 Topology- and Performance-Aware Collectives 100

6.6 Topology as Input 101

6.7 Performance as Input 102

6.8 Non-MPI Collective Algorithms for Heterogeneous Networks 106

6.9 Conclusion 111

7. Effective Data Access Patterns on Massively Parallel Processors 115
Gabriele Capannini, Ranieri Baraglia, Fabrizio Silvestri, and Franco Maria Nardini

7.1 Introduction 115

7.2 Architectural Details 116

7.3 K-Model 117

7.4 Parallel Prefix Sum 120

7.5 Bitonic Sorting Networks 126

7.6 Final Remarks 132

8. Scalable Storage I/O Software for Blue Gene Architectures 135
Florin Isaila, Javier Garcia, and Jesús Carretero

8.1 Introduction 135

8.2 Blue Gene System Overview 136

8.3 Design and Implementation 138

8.4 Conclusions and Future Work 142

PART IV EFFICIENT EXPLOITATION OF HETEROGENEOUS ARCHITECTURES 145

9. Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems 147
Hamid Arabnejad, Jorge G. Barbosa, and Frédéric Suter

9.1 Introduction 148

9.2 Concurrent Workflow Scheduling 153

9.3 Experimental Results and Discussion 160

9.4 Conclusions 165

10. Systematic Mapping of Reed–Solomon Erasure Codes on Heterogeneous Multicore Architectures 169
Roman Wyrzykowski, Marcin Wozniak, and Lukasz Kuczynski

10.1 Introduction 169

10.2 Related Works 171

10.3 Reed–Solomon Codes and Linear Algebra Algorithms 172

10.4 Mapping Reed–Solomon Codes on Cell/B.E. Architecture 173

10.5 Mapping Reed–Solomon Codes on Multicore GPU Architectures 178

10.6 Methods of Increasing the Algorithm Performance on GPUs 181

10.7 GPU Performance Evaluation 185

10.8 Conclusions and Future Works 190

11. Heterogeneous Parallel Computing Platforms and Tools for Compute-Intensive Algorithms: A Case Study 193
Daniele D'Agostino, Andrea Clematis, and Emanuele Danovaro

11.1 Introduction 194

11.2 A Low-Cost Heterogeneous Computing Environment 196

11.3 First Case Study: The N-Body Problem 200

11.4 Second Case Study: The Convolution Algorithm 206

11.5 Conclusions 211

12. Efficient Application of Hybrid Parallelism in Electromagnetism Problems 215
Alejandro Alvarez-Melcon, Fernando D. Quesada, Domingo Gimenez, Carlos Pérez-Alcaraz, Jose-Gines Picon, and Tomas Ramírez

12.1 Introduction 215

12.2 Computation of Green’s functions in Hybrid Systems 216

12.3 Parallelization in Numa Systems of a Volume Integral Equation Technique 222

12.4 Autotuning Parallel Codes 226

12.5 Conclusions and Future Research 230

PART V CPU + GPU COPROCESSING 235

13. Design and Optimization of Scientific Applications for Highly Heterogeneous and Hierarchical HPC Platforms Using Functional Computation Performance Models 237
David Clarke, Aleksandar Ilic, Alexey Lastovetsky, Vladimir Rychkov, Leonel Sousa, and Ziming Zhong

13.1 Introduction 238

13.2 Related Work 241

13.3 Data Partitioning Based on Functional Performance Model 243

13.4 Example Application: Heterogeneous Parallel Matrix Multiplication 245

13.5 Performance Measurement on CPUs/GPUs System 247

13.6 Functional Performance Models of Multiple Cores and GPUs 248

13.7 FPM-Based Data Partitioning on CPUs/GPUs System 250

13.8 Efficient Building of Functional Performance Models 251

13.9 FPM-Based Data Partitioning on Hierarchical Platforms 253

13.10 Conclusion 257

14. Efficient Multilevel Load Balancing on Heterogeneous CPU + GPU Systems 261
Aleksandar Ilic and Leonel Sousa

14.1 Introduction: Heterogeneous CPU + GPU Systems 262

14.2 Background and Related Work 265

14.3 Load Balancing Algorithms for Heterogeneous CPU + GPU Systems 269

14.4 Experimental Results 275

14.5 Conclusions 279

15. The All-Pair Shortest-Path Problem in Shared-Memory Heterogeneous Systems 283
Hector Ortega-Arranz, Yuri Torres, Diego R. Llanos, and Arturo Gonzalez-Escribano

15.1 Introduction 283

15.2 Algorithmic Overview 285

15.3 CUDA Overview 287

15.4 Heterogeneous Systems and Load Balancing 288

15.5 Parallel Solutions to The APSP 289

15.6 Experimental Setup 291

15.7 Experimental Results 293

15.8 Conclusions 297

PART VI EFFICIENT EXPLOITATION OF DISTRIBUTED SYSTEMS 301

16. Resource Management for HPC on the Cloud 303
Marc E. Frincu and Dana Petcu

16.1 Introduction 303

16.2 On the Type of Applications for HPC and HPC2 305

16.3 HPC on the Cloud 306

16.4 Scheduling Algorithms for HPC2 311

16.5 Toward an Autonomous Scheduling Framework 312

16.6 Conclusions 319

17. Resource Discovery in Large-Scale Grid Systems 323
Konstantinos Karaoglanoglou and Helen Karatza

17.1 Introduction and Background 323

17.2 The Semantic Communities Approach 325

17.3 The P2P Approach 329

17.4 The Grid-Routing Transferring Approach 333

17.5 Conclusions 337

PART VII ENERGY AWARENESS IN HIGH-PERFORMANCE COMPUTING 341

18. Energy-Aware Approaches for HPC Systems 343
Robert Basmadjian, Georges Da Costa, Ghislain Landry Tsafack Chetsa, Laurent Lefevre, Ariel Oleksiak, and Jean-Marc Pierson

18.1 Introduction 344

18.2 Power Consumption of Servers 345

18.3 Classification and Energy Profiles of HPC Applications 354

18.4 Policies and Leverages 359

18.5 Conclusion 360

19. Strategies for Increased Energy Awareness in Cloud Federations 365
Gabor Kecskemeti, AttilaKertesz, Attila Cs. Marosi, and Zsolt Nemeth

19.1 Introduction 365

19.2 Related Work 367

19.3 Scenarios 369

19.4 Energy-Aware Cloud Federations 374

19.5 Conclusions 379

20. Enabling Network Security in HPC Systems Using Heterogeneous CMPs 383
Ozcan Ozturk and Suleyman Tosun

20.1 Introduction 384

20.2 Related Work 386

20.3 Overview of Our Approach 387

20.4 Heterogeneous CMP Design for Network Security Processors 390

20.5 Experimental Evaluation 394

20.6 Concluding Remarks 397

PART VIII APPLICATIONS OF HETEROGENEOUS HIGH-PERFORMANCE COMPUTING 401

21. Toward a High-Performance Distributed CBIR System for Hyperspectral Remote Sensing Data: A Case Study in Jungle Computing 403
Timo van Kessel, NielsDrost, Jason Maassen, Henri E. Bal, Frank J. Seinstra, and Antonio J. Plaza

21.1 Introduction 404

21.2 CBIR For Hyperspectral Imaging Data 407

21.3 Jungle Computing 410

21.4 IBIS and Constellation 412

21.5 System Design and Implementation 415

21.6 Evaluation 420

21.7 Conclusions 426

22. Taking Advantage of Heterogeneous Platforms in Image and Video Processing 429
Sidi A. Mahmoudi, Erencan Ozkan, Pierre Manneback, and Suleyman Tosun

22.1 Introduction 430

22.2 Related Work 431

22.3 Parallel Image Processing on GPU 433

22.4 Image Processing on Heterogeneous Architectures 437

22.5 Video Processing on GPU 438

22.6 Experimental Results 444

22.7 Conclusion 447

23. Real-Time Tomographic Reconstruction Through CPU + GPU Coprocessing 451
Jose Ignacio Agulleiro, Francisco Vazquez, Ester M. Garzon, and Jose J. Fernandez

23.1 Introduction 452

23.2 Tomographic Reconstruction 453

23.3 Optimization of Tomographic Reconstruction for CPUs and for GPUs 455

23.4 Hybrid CPU + GPU Tomographic Reconstruction 457

23.5 Results 459

23.6 Discussion and Conclusion 461

Acknowledgments 463

References 463

Index 467

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Author Information

Emmanuel Jeannot is a Senior Research Scientist at INRIA. He received his PhD in computer science from Ecole Normale Superieur 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 in Vilnius, Lithuania. His research interests include parallel computing, optimization, data analysis and visualization.

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