High-performance computing on complex environments:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
2014
|
| Schriftenreihe: | Wiley series on parallel and distributed computing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Buchcover |
| Beschreibung: | Includes bibliographical references and index Weitere Ausgabe: Online version : High performance computing on complex environments |
| Beschreibung: | XXIX, 468 S. graph. Darst. |
| ISBN: | 9781118712054 9781118712078 |
Internformat
MARC
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| 245 | 1 | 0 | |a High-performance computing on complex environments |c [ed. by] Emmanuel Jeannot ; Julius Zilinskas |
| 264 | 1 | |a Hoboken, N.J. |b Wiley |c 2014 | |
| 300 | |a XXIX, 468 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series on parallel and distributed computing | |
| 500 | |a Includes bibliographical references and index | ||
| 500 | |a Weitere Ausgabe: Online version : High performance computing on complex environments | ||
| 650 | 0 | 7 | |a Hochleistungsrechnen |0 (DE-588)4532701-4 |2 gnd |9 rswk-swf |
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| 700 | 1 | |a Jeannot, Emmanuel |e Sonstige |0 (DE-588)1053779267 |4 oth | |
| 700 | 1 | |a Žilinskas, Julius |d 1973- |e Sonstige |0 (DE-588)1029936498 |4 oth | |
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Datensatz im Suchindex
| _version_ | 1804152324940103680 |
|---|---|
| adam_text | 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
Žilinskas
1.1
Introduction and Vision
/ 4
1.2
Scientific Organization
/ 6
1.2.1
Scientific Focus
/ 6
1.2.2
Working Groups
/ 6
1.3
Activities of the Project
/ 6
1.3.1
Spring Schools
/ 6
1.3.2
International Workshops
/ 7
1.3.3
Working Groups Meetings
/ 7
1.3.4
Management Committee Meetings
/ 7
1.3.5
Short-Term Scientific Missions
/ 7
1.4
Main Outcomes of the Action
/ 7
1.5
Contents of the Book
/ 8
Acknowledgment
/ 10
• ·
VII
ViÜ CONTENTS
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 ^
Dimitar Lukarski and Maya Neytcheva
2.1
Introduction
/ 14
2.2
General Description of Iterative Methods and Preconditioning
/ 16
2.2.1
Basic Iterative Methods
/ 16
2.2.2
Projection Methods:
CG
and GMRES
/
I
8
2.3
Preconditioning Techniques
/ 20
2.4
Defect-Correction Technique
/ 21
2.5
Multigrid Method
/ 22
2.6
Paral
lei i zad
on of Iterative Methods
/ 22
2.7
Heterogeneous Systems
/ 23
2.7.1
Heterogeneous Computing
/ 24
2.7.2
Algorithm Characteristics and Resource Utilization
/ 25
2.7.3
Exposing Parallelism
/ 26
2.7.4
Heterogeneity in Matrix Computation
/ 26
2.7.5
Setup of Heterogeneous Iterative Solvers
/ 27
2.8
Maintenance and Portability
/ 29
2.9
Conclusion
/ 30
Acknowledgments
/ 31
References
/ 31
3.
Efficient Numerical Solution of 2D Diffusion Equation on Multicore
Computers
33
Matjai
Depolli,
Gregor
Kosec,
and Roman Trobec
3.1
Introduction
/ 34
3.2
Test Case
/ 35
3.2.1
Governing Equations
/ 35
3.2.2
Solution Procedure
/ 36
3.3
Parallel Implementation
/ 39
3.3.1
Intel PCM Library
/ 39
3.3.2
OpenMP
/ 40
CONTENTS
ІХ
3.4
Results
/ 41
3.4.1
Results of Numerical
Integration / 41
3.4.2
Parallel Efficiency
/ 42
3.5
Discussion
/ 45
3.6
Conclusion
/ 47
Acknowledgment
/ 47
References
/ 47
4.
Parallel Algorithms for Parabolic Problems on Graphs in
Neuroscience
51
Natal
iju
Tumanova
and Raimondas
Čiegis
4.1
Introduction
/ 51
4.2
Formulation of the Discrete Model
/ 53
4.2.1
The ^-Implicit Discrete Scheme
/ 55
4.2.2
The Predictor-Corrector Algorithm
1/57
4.2.3
The Predictor-Corrector Algorithm II
/ 58
4.3
Parallel Algorithms
/ 59
4.3.1
Parallel ^-Implicit Algorithm
/ 59
4.3.2
Parallel Predictor-Corrector Algorithm I
/ 62
4.3.3
Parallel Predictor-Corrector Algorithm II
/ 63
4.4
Computational Results
/ 63
4.4.1
Experimental Comparison of Predictor-Corrector
Algorithms
/ 66
4.4.2
Numerical Experiment of Neuron Excitation
/ 68
4.5
Conclusions
/ 69
Acknowledgments
/ 70
References
/ 70
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
5.2.1
A Key to Scalability: Data Locality
/ 77
Г
CONTENTS
5.2.2
Data Locality
Management
in Parallel Programming
Models
/ 77
5.2.3
Virtual Topology: Definition and Characteristics
/ 78
5.2.4
Understanding the Hardware
/ 79
5.3
Formalization of the Problem
/ 79
5.4
Algorithmic Strategies for Topology Mapping
/ 81
5.4.1
Greedy Algorithm Variants
/ 81
5.4.2
Graph Partitioning
/ 82
5.4.3
Schemes Based on Graph Similarity
/ 82
5.4.4
Schemes Based on Subgraph Isomorphism
/ 82
5.5
Mapping Enforcement Techniques
/ 82
5.5.1
Resource Binding
/ 83
5.5.2
Rank Reordering
/ 83
5.5.3
Other Techniques
/ 84
5.6
Survey of Solutions
/ 85
5.6.1
Algorithmic Solutions
/ 85
5.6.2
Existing Implementations
/ 85
5.7
Conclusion and Open Problems
/ 89
Acknowledgment
/ 90
References
/ 90
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
6.4.1
Comparison to Homogeneous Clusters
/ 99
6.5
Topology-and Performance-Aware Collectives
/ 100
6.6
Topology as Input
/ 101
6.7
Performance as Input
/ 102
6.7.1
Homogeneous Performance Models
/ 103
6.7.2
Heterogeneous Performance Models
/ 105
CONTENTS
ХІ
6.7..3
Estimation
of Parameters of Heterogeneous Performance
Models
/ 106
6.7.4
Other Performance Models
/ 106
6.8
Non-MPI
Collective Algorithms for Heterogeneous Networks
/ 106
6.8.1
Optimal Solutions with Multiple Spanning Trees
/ 107
6.8.2
Adaptive Algorithms for Efficient Large-Message
Transfer
/107
6.8.3
Network Models Inspired by
В
itTorrent
/ 108
6.9
Conclusion /111
Acknowledgments /111
References /111
7.
Effective Data Access Patterns on Massively Parallel Processors
115
Gabriele Capannini,
Ranieri Baraglia,
Fabrizio
Silvestri,
and Franco Maria
Nard
ini
7.1
Introduction
/ 115
7.2
Architectural Details
/ 116
7.3
K-Model
/ 117
7.3.1
The Architecture
/ 117
7.3.2
Cost and Complexity Evaluation
/ 118
7.3.3
Efficiency Evaluation
/ 119
7.4
Parallel Prefix Sum
/ 120
7.4.1
Experiments
/ 125
7.5
В
itonic Sorting Networks
/ 126
7.5.1
Experiments
/ 131
7.6
Final Remarks
/ 132
Acknowledgments
/ 133
References
/ 133
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
8.2.1
Blue Gene Architecture
/ 136
8.2.2
Operating System Architecture
/ 136
Х/7
CONTENTS
8.3
Design and Implementation
/ 138
8.3.1
The Client Module
/ 139
8.3.2
The I/O Module
/ 141
8.4
Conclusions and Future Work
/ 142
Acknowledgments
/ 142
References
/ 142
PART IV EFFICIENT EXPLOITATION OF HETEROGENEOUS
ARCHITECTURES
9.
Fair Resource Sharing for Dynamic Scheduling of Workflows on
Heterogeneous Systems
Hamid
Arabnejad, Jorge G.
Barbosa,
and
Frédéric
Sute
r
9.1
Introduction
/ 148
9.1.1
Application Model
/ 148
9.1.2
System Model
/151
9.1.3
Performance Metrics
/ 152
9.2
Concurrent Workflow Scheduling
/ 153
9.2.1
Offline Scheduling of Concurrent Workflows
/ 154
9.2.2
Online Scheduling of Concurrent Workflows
/ 155
9.3
Experimental Results and Discussion
/ 160
9.3.1
DAG Structure
/ 160
9.3.2
Simulated Platforms
/ 160
9.3.3
Results and Discussion
/ 162
9.4
Conclusions
/ 165
Acknowledgments
/ 166
References
/166
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
10.4.1
Cell/B.E. Architecture
/ 173
CONTENTS
ХНІ
10.4.2
Basic
Assumptions for Mapping
/ 174
10.4.3
Vectorization Algorithm and Increasing its Efficiency
/ 175
10.4.4
Performance Results
/ 177
10.5
Mapping Reed-Solomon Codes on Multicore GPU
Architectures
/ 178
10.5.1
Paral
lel ization
of Reed-Solomon Codes on GPU
Architectures
/ 178
10.5.2
Organization of GPU Threads
/ 180
10.6
Methods of Increasing the Algorithm Performance on GPUs
/ 181
10.6.1
Basic Modifications
/ 181
10.6.2
Stream Processing
/ 182
10.6.3
Using Shared Memory
/ 184
10.7
GPU Performance Evaluation
/ 185
10.7.1
Experimental Results
/ 185
10.7.2
Performance Analysis using the Roofline Model
/ 187
10.8
Conclusions and Future Works
/ 190
Acknowledgments
/ 191
References
/ 191
11.
Heterogeneous Parallel Computing Platforms and Tools for
Compute-Intensive Algorithms: A Case Study
193
Daniele
D Agostino,
Andrea Clematis, and
Emanuele Danovaw
11.1
Introduction
/ 194
11.2
A Low-Cost Heterogeneous Computing Environment
/ 196
11.2.1
Adopted Computing Environment
/199
11.3
First Case Study: The /V-Body Problem
/ 200
11.3.1
The Sequential TV-Body Algorithm
/ 201
11.3.2
The Parallel TV-Body Algorithm for Multicore
Architectures
/ 203
11.3.3
The Parallel /V-Body Algorithm for
CUDA
Architectures
/ 204
11.4
Second Case Study: The Convolution Algorithm
/ 206
11.4.1
The Sequential Convolver Algorithm
/ 206
11.4.2
The Parallel Convolver Algorithm for Multicore
Architectures
/ 207
11.4.3
The Parallel Convolver Algorithm for GPU
Architectures
/ 208
XIV
CONTENTS
11.5
Conclusions
/211
Acknowledgments
/212
References
/212
12.
Efficient Application of Hybrid Parallelism in Electromagnetism
Problems 215
Alejandro Alvarez-
M
elcón,
Fernando
D.
Quesada,
Domingo
Giménez.
Carlos
Pérez-Alcaraz, José-Ginés Picón, and Tomás Ramírez
12.1
Introduction
/215
12.2
Computation of Green s functions in Hybrid vSystcms
/216
12.2.1
Computation in a Heterogeneous Cluster
/217
12.2.2
Experiments
/218
12.3
Paralleli
zation in
Numa
Systems of a Volume Integral liquation
Technique
/ 222
12.3.1
Experiments
/ 222
12.4 Autotuning
Parallel Codes
/ 226
12.4.1
Empirical
Autotuning / 227
12.4.2
Modeling the Linear Algebra Routines
/ 229
12.5
Conclusions and Future Research
/ 230
Acknowledgments
/ 231
References
/ 232
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
Ніс,
Alexey Lastovetsfcy, 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
CONTENTS
XV
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
Acknowledgments
/ 259
References
/ 259
14.
Efficient Multilevel Load Balancing on Heterogeneous CPU
+
GPU
Systems
261
Aleksandar
¡lic
and
Leonel Sousa
14.1
Introduction: Heterogeneous CPU
+
GPU Systems
/ 262
14.1.1
Open Problems and Specific Contributions
/ 263
14.2
Background and Related Work
/ 265
14.2.1
Divisible Load Scheduling in Distributed CPU-Only
Systems
/ 265
14.2.2
Scheduling in Multicore CPU and Multi-GPU
Environments
/ 268
14.3
Load Balancing Algorithms for Heterogeneous CPU
+
GPU
Systems
/ 269
14.3.1
Multilevel Simultaneous Load Balancing Algorithm
/ 270
14.3.2
Algorithm for Multi-Installment Processing with
Maldistributions
/ 273
14.4
Experimental Results
/ 275
14.4.1
MSLBA Evaluation: Dense Matrix Multiplication Case
Study
/ 275
14.4.2
AMPMD Evaluation: 2D FFT Case Study
/ 277
14.5
Conclusions
/ 279
Acknowledgments
/ 280
References
/ 280
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
XVI
CONTENTS
15.2
Algori
thmic
Overview
/ 285
15.2.1 Graph
Theory
Notation / 285
15.2.2
Dijkstra s
Algorithm
/ 286
15.2.3
Parallel Version of
Dijkstra s
Algorithm
/ 287
15.3
CUDA
Overview
/ 287
15.4
Heterogeneous Systems and Load Balancing
/ 288
15.5
Parallel Solutions to The APSP
/ 289
15.5.1
GPU Implementation
/ 289
15.5.2
Heterogeneous Implementation
/ 290
15.6
Experimental Setup
/ 291
15.6.1
Methodology
/ 291
15.6.2
Target Architectures
/ 292
15.6.3
Input Set Characteristics
/ 292
15.6.4
Load-Balancing Techniques Evaluated
/ 292
15.7
Experimental Results
/ 293
15.7.1
Complete APSP
/ 293
15.7.2
Sn-Source-Node-to-All Shortest Path
/ 295
15.7.3
Experimental Conclusions
/ 296
15.8
Conclusions
/ 297
Acknowledgments
/ 297
References
/ 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
16.3.1
General PaaS Solutions
/ 306
16.3.2
On-Demand Platforms for HPC
/ 310
16.4
Scheduling Algorithms for HPC2
/311
16.5
Toward an Autonomous Scheduling Framework
/312
16.5.1
Autonomous Framework for RMS
/313
CONTENTS XVÜ
16.5.2
Self-Management
/315
16.5.3
Use Cases
/317
16.6
Conclusions
/319
Acknowledgment
/ 320
References
/ 320
17.
Resource Discovery in Large-Scale Grid Systems
323
Konstantinos Karaoglanoglou
and Helen Karatza
17.1
Introduction and Background
/ 323
17.1.1
Introduction
/ 323
17.1.2
Resource Discovery in Grids
/ 324
17.1.3
Background
/ 325
17.2
The Semantic Communities Approach
/ 325
17.2.1
Grid Resource Discovery Using Semantic Communities
/ 325
17.2.2
Grid Resource Discovery Based on Semantically Linked
Virtual Organizations
/ 327
17.3
The P2P Approach
/ 329
17.3.1
On Fully Decentralized Resource Discovery in Grid
Environments Using a P2P Architecture
/ 329
17.3.2
P2P Protocols for Resource Discovery in the Grid
/ 330
17.4
The Grid-Routing Transferring Approach
/ 333
17.4.1
Resource Discovery Based on Matchmaking Routers
/ 333
17.4.2
Acquiring Knowledge in a Large-Scale Grid System
/ 335
17.5
Conclusions
/ 337
Acknowledgment
/ 338
References
/338
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,
Ariei Oleksiak,
and Jean-Marc Pierson
18.1
Introduction
/ 344
18.2
Power Consumption of Servers
/ 345
18.2.1
Server Modeling
/ 346
CONTENTS
18.2.2 Power
Prediction
Models / 347
18.3
Classification
and Energy
Profiles of HPC Applications
/ 354
18.3.1
Phase Detection
/ 356
Î
8.3.2
Phase Identification
/ 358
18.4
Policies and Leverages
/ 359
18.5
Conclusion
/ 360
Acknowledgements
/ 361
References
/ 361
19.
Strategies for Increased Energy Awareness in Cloud Federations
365
Gabor
Kecskeméti, Attila Kertész, Attila
Cs.
Marosi, und
/.solt
Németh
19.1
Introduction
/ 365
19.2
Related Work
/ 367
19.3
Scenarios /
369
19.3.1
Increased
Energy
Awareness Across Multiple Data Centers
within a Single Administrative Domain
/ 369
19.3.2
Energy Considerations in Commercial Cloud
Federations
/ 372
19.3.3
Reduced Energy Footprint of Academic Cloud
Federations
/ 374
19.4
Energy-Aware Cloud Federations
/ 374
19.4.1
Availability of Energy-Consumption-Reiated
Information
/ 375
19.4.2
Service Call Scheduling at the Meta-Brokering Level of
FCM
/ 376
19.4.3
Service Call Scheduling and VM Management at the
Cloud-Brokering Level of FCM
/ 377
19.5
Conclusions
/ 379
Acknowledgments
/380
References
/ 380
20.
Enabling Network Security in HPC Systems Using Heterogeneous
CMPs
Ozcan Ozturk and Suleyman Tosun
20.1
Introduction
/384
20.2
Related Work
/ 386
CONTENTS
ХІХ
20.3
Overview of Our Approach
/ 387
20.3.1
Heterogeneous CMP Architecture
/ 387
20.3.2
Network Security Application Behavior
/388
20.3.3
High-Level View
/ 389
20.4
Heterogeneous CMP Design for Network Security Processors
/ 390
20.4.1
Task Assignment
/ 390
20.4.2
ILP Formulation
/ 391
20.4.3
Discussion
/ 393
20.5
Experimental Evaluation
/ 394
20.5.1
Setup
/ 394
20.5.2
Results
/ 395
20.6
Concluding Remarks
/ 397
Acknowledgments
/ 397
References
/ 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,
Niels
Drost,
Jason Maassen, Henri
E. Bal,
Frank
J. Seinstra,
and Antonio J. Plaza
21.1
Introduction
/ 404
21.2
CBIR For Hyperspectral Imaging Data
/ 407
21.2.1
Spectral Unmixing
/ 407
21.2.2
Proposed CBIR System
/ 409
21.3
Jungle Computing
/ 410
21.3.1
Jungle Computing: Requirements
/411
21.4
IBIS and Constellation
/ 412
21.5
System Design and Implementation
/415
21.5.1
Endmember Extraction
/418
21.5.2
Query Execution
/ 418
21.5.3
Equi-Kernels
/419
21.5.4
Matchmaking
/ 420
21.6
Evaluation
/ 420
21.6.1
Performance Evaluation
/ 421
XX
CONTENTS
21.7
Conclusions
/ 426
Acknowledgments /
426
References
/ 426
22.
Taking Advantage of Heterogeneous Platforms in Image and Video
Processing
Sidi
A. Mahmoudi, Erencan Ozkan, Pierre
Manneback,
and Suleyman Tosun
22.1
Introduction
/ 430
22.2
Related Work
/ 431
22.2.1
Image Processing on GPU
/ 431
22.2.2
Video Processing on GPU
/ 432
22.2.3
Contribution
/ 433
22.3
Parallel Image Processing on GPU
/433
22.3.1
Development Scheme for Image Processing on GPU
/ 433
22.3.2
GPU Optimization
/ 434
22.3.3
GPU Implementation of Edge and Corner Detection
/ 434
22.3.4
Performance Analysis and Evaluation
/ 434
22.4
Image Processing on Heterogeneous Architectures
/ 437
22.4.1
Development Scheme for Multiple Image Processing
/ 437
22.4.2
Task Scheduling within Heterogeneous Architectures
/ 438
22.4.3
Optimization Within Heterogeneous Architectures
/ 438
22.5
Video Processing on GPU
/438
22.5.1
Development Scheme for Video Processing on GPU
/439
22.5.2
GPU Optimizations
/ 440
22.5.3
GPU Implementations
/ 440
22.5.4
GPU-Based Silhouette Extraction
/ 440
22.5.5
GPU-Based Optical Flow Estimation
/ 440
22.5.6
Result Analysis
/ 443
22.6
Experimental Results
/ 444
22.6.1
Heterogeneous Computing for Vertebra Segmentation
/ 444
22.6.2
GPU Computing for Motion Detection Using a Moving
Camera
/ 445
22.7
Conclusion
/ 447
Acknowledgment
/ 448
References
/ 448
CONTENTS
XXI
23.
Real-Time
Tomographie
Reconstrucción
Through CPU
+
GPU
Coprocessing
451
José Ignacio
Agulleiro, Francisco Vazquez, Ester M.
Garzon,
and Jose J. Fernandez
23.1
Introduction
/ 452
23.2 Tomographie
Reconstruction
/ 453
23.3
Optimization of
Tomographie
Reconstruction for CPUs and for
GPUs
/ 455
23.4
Hybrid CPU
+
GPU
Tomographie
Reconstruction
/ 457
23.5
Results
/ 459
23.6
Discussion and Conclusion
/ 461
Acknowledgments
/ 463
References
/463
Index
467
|
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| spelling | High-performance computing on complex environments [ed. by] Emmanuel Jeannot ; Julius Zilinskas Hoboken, N.J. Wiley 2014 XXIX, 468 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series on parallel and distributed computing Includes bibliographical references and index Weitere Ausgabe: Online version : High performance computing on complex environments Hochleistungsrechnen (DE-588)4532701-4 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Hochleistungsrechnen (DE-588)4532701-4 s DE-604 Jeannot, Emmanuel Sonstige (DE-588)1053779267 oth Žilinskas, Julius 1973- Sonstige (DE-588)1029936498 oth Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389307&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389307&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Buchcover |
| spellingShingle | High-performance computing on complex environments Hochleistungsrechnen (DE-588)4532701-4 gnd |
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| title | High-performance computing on complex environments |
| title_auth | High-performance computing on complex environments |
| title_exact_search | High-performance computing on complex environments |
| title_full | High-performance computing on complex environments [ed. by] Emmanuel Jeannot ; Julius Zilinskas |
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| title_full_unstemmed | High-performance computing on complex environments [ed. by] Emmanuel Jeannot ; Julius Zilinskas |
| title_short | High-performance computing on complex environments |
| title_sort | high performance computing on complex environments |
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