HPC@Green IT

Green High Performance Computing Methods

By: Ralf Gruber, Vincent Keller, Erich Strohmaier (Foreword by)

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Hardcover | 24 March 2010

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240 Pages

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23.5 x 16.51 x 1.91

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Making the most ef?cient use of computer systems has rapidly become a leading topic of interest for the computer industry and its customers alike. However, the focus of these discussions is often on single, isolated, and speci?c architectural and technological improvements for power reduction and conservation, while ignoring the fact that power ef?ciency as a ratio of performance to power consumption is equally in?uenced by performance improvements and architectural power red- tion. Furthermore, ef?ciency can be in?uenced on all levels of today's system hi- archies from single cores all the way to distributed Grid environments. To improve execution and power ef?ciency requires progress in such diverse ?elds as program optimization, optimization of program scheduling, and power reduction of idling system components for all levels of the system hierarchy. Improving computer system ef?ciency requires improving system performance and reducing system power consumption. To research and reach reasonable conc- sions about system performance we need to not only understand the architectures of our computer systems and the available array of code transformations for p- formance optimizations, but we also need to be able to express this understanding in performance models good enough to guide decisions about code optimizations for speci?c systems. This understanding is necessary on all levels of the system hierarchy from single cores to nodes to full high performance computing (HPC) systems, and eventually to Grid environments with multiple systems and resources.

Industry Reviews

From the reviews:

"The current book "HPC@Green IT" by Ralf Gruber and Vincent Keller is unique in addressing all of these topics in a coherent and systematic way and in doing so fills an important gap. The methods presented and their integration have the potential to influence power efficiency and consumption on various scales of the system architectures and should ultimately help the 'greening' of HPC computing. "

Dr. Erich Strohmaier, Lawrence Berkeley Laboratory, August 2009

"The book familiarizes readers with this topic and provides a collection of techniques for green high-performance computing (HPC). ... In summary, this well-written book includes a lot of colorful graphs/charts, tables, and benchmark results. ... Whereas the authors mainly discuss techniques like frequency scaling (to reduce power usage) and compiler optimization (to reduce execution time), there are other techniques that can improve a system's efficiency--for example, dynamic powering on/off servers." (Michele Mazzucco, ACM Computing Reviews, August, 2010)

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You Can Find This Book In

Non-Fiction Mathematics Calculus & Mathematical Analysis Numerical Analysis Computing & I.T.Computer Programming & Software Development Software Engineering Computer Hardware Grid & Parallel Computing Supercomputers Computer Science Systems Analysis & Design

Computer Networking & Communications Operating Systems Digital Lifestyle & Online World: Consumer & User Guides Portable & Handheld Devices User Guides

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Paperback

Published: 26th November 2014

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Introduction	p. 1
Basic goals of the book	p. 1
What do I get for one Watt today?	p. 1
Main memory bottleneck	p. 3
Optimize resource usage	p. 3
Application design	p. 4
Organization of the book	p. 4
Historical aspects	p. 4
Parameterization	p. 5
Models	p. 5
Core optimization	p. 6
Node optimization	p. 6
Cluster optimization	p. 6
Grid-brokering to save energy	p. 7
Historical highlights	p. 9
Evolution of computing	p. 9
The first computer companies	p. 14
ERA, EMCC and Univac	p. 14
Control Data Corporation, CDC	p. 14
Cray Research	p. 15
Thinking Machines Corporation	p. 16
International Business Machines (IBM)	p. 17
The ASCI effort	p. 18
The Japanese efforts	p. 19
The computer generations	p. 20
The evolution in computing performance	p. 20
Performance/price evolution	p. 22
Evolution of basic software	p. 22
Evolution of algorithmic complexity	p. 23
The TOP500 list	p. 25
Outlook with the TOP500 curves	p. 27
The GREEN500 List	p. 28
Proposal for a REAL500 list	p. 30
Parameterization	p. 31
Definitions	p. 31
Parameterization of applications	p. 35
Application parameter set	p. 35
Parameterization of BLAS library routines	p. 36
SMXV: Parameterization of sparse matrix*vector operation	p. 38
Parameterization of a computational nodes P_i ∈ r_i	p. 39
Parameterization of the interconnection networks	p. 41
Types of networks	p. 41
Parameterization of clusters and networks	p. 42
Parameters related to running applications	p. 44
Conclusion	p. 47
Models	p. 49
The performance prediction model	p. 49
The execution time evaluation model (ETEM)	p. 53
A network performance model	p. 53
The extended ¿ - ¿ model	p. 55
Validation of the models	p. 56
Methodology	p. 56
Example: The full matrix*matrix multiplication DGEMM	p. 57
Example: Sparse matrix*vector multiplication SMXV	p. 59
Core optimization	p. 63
Some useful notions	p. 63
Data hierarchy	p. 63
Data representation	p. 64
Floating point operations	p. 67
Pipelining	p. 68
Single core optimization	p. 70
Single core architectures	p. 70
Memory conflicts	p. 70
Indirect addressing	p. 74
Unrolling	p. 75
Dependency	p. 76
Inlining	p. 78
If statement in a loop	p. 78
Code porting aspects	p. 79
How to develop application software	p. 83
Application to plasma physics codes	p. 84
Tokamaks and Stellerators	p. 84
Optimization of VMEC	p. 88
Optimization of TERPSICHORE	p. 91
Conclusions for single core optimization	p. 94
Node optimization	p. 95
Shared memory computer architectures	p. 95
SMP/NUMA architectures	p. 95
The Cell	p. 99
GPGPU for HPC	p. 100
Node comparison and OpenMP	p. 105
Race condition with OpenMP	p. 109
Application optimization with OpenMP: the 3D Helmholtz solver	p. 110
Fast Helmholtz solver for parallelepipedic geometries	p. 111
NEC SX-5 reference benchmark	p. 113
Single processor benchmarks	p. 114
Parallelizalion with OpenMP	p. 115
Parallelizalion with MPI	p. 115
Conclusion	p. 119
Application optimization with OpenMP: TERPSICHORE	p. 119
Cluster optimization	p. 121
Introduction on parallelization	p. 121
Internode communication networks	p. 121
Network architectures	p. 121
Comparison between network architectures	p. 129
Distributed memory parallel computer architectures	p. 131
Integrated parallel computer architectures	p. 131
Commodity cluster architectures	p. 134
Energy consumption issues	p. 136
The issue of resilience	p. 137
Type of parallel applications	p. 138
Embarrassingly parallel applications	p. 138
Applications with point-to-point communications	p. 138
Applications with multicast communication needs	p. 139
Shared memory applications (OpenMP)	p. 139
Components based applications	p. 139
Domain decomposition techniques	p. 139
Test example: The Gyrotron	p. 140
The geometry and the mesh	p. 142
Connectivity conditions	p. 142
Parallel matrix solver	p. 143
The electrostatic precipitator	p. 145
Scheduling of parallel applications	p. 146
Static scheduling	p. 146
Dynamic scheduling	p. 146
SpecuLOOS	p. 147
Introduction	p. 147
Test case description	p. 147
Complexity on one node	p. 149
Wrong complexity on the Blue Gene/L	p. 150
Fine results on the Blue Gene/L	p. 151
Conclusions	p. 151
TERPSICHORE	p. 153
Parallelization of the LEMan code with MPI and OpenMP	p. 154
Introduction	p. 154
Parallelization	p. 154
CPU time results	p. 156
Conclusions	p. 159
Grid-level Brokering to save energy	p. 161
About Grid resource brokering	p. 161
An Introduction to ïanos	p. 162
Job Submission Scenario	p. 164
The cost model	p. 165
Mathematical formulation	p. 165
CPU costs K_e	p. 167
License fees K_l	p. 169
Costs due to waiting time K_w	p. 169
Energy costs K_eco	p. 169
Data transfer costs K_d	p. 171
Example: The Pleiades clusters CPU cost per hour	p. 171
Different currencies in a Grid environment	p. 173
The implementation	p. 173
Architecture & Design	p. 174
The Grid Adapter	p. 174
The Meta Scheduling Service (MSS)	p. 175
The Resource Broker	p. 176
The System Information	p. 177
The Data Warehouse	p. 177
The Monitoring Service	p. 177
The Monitoring Module VAMOS	p. 178
Integration with UNICORL Grid System	p. 179
Scheduling algorithm	p. 179
User Interfaces to the ïanos framework	p. 181
DVS-able processors	p. 182
Power consumption of a CPU	p. 183
An algorithm to save energy	p. 184
First results with SMXV	p. 185
A first implementation	p. 186
Conclusions	p. 188
Recommendations	p. 189
Application oriented recommendations	p. 189
Code development	p. 189
Code validation	p. 189
Porting codes	p. 190
Optimizing parallelized applications	p. 190
Race condition	p. 190
Hardware and basic software aspects	p. 191
Basic software	p. l91
Choice of system software	p. 192
Energy reduction	p. 192
Processor frequency adaptation	p. 192
Improved cooling	p. 193
Choice of optimal resources	p. 193
Best choice of new computer	p. 193
Last but not least	p. 194
Miscellaneous	p. 194
Course material	p. 194
A new REAL500 List	p. 194
Glossary	p. 197
References	p. 205
About the authors	p. 213
Index	p. 215
Table of Contents provided by Ingram. All Rights Reserved.

HPC@Green IT

Green High Performance Computing Methods

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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