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作者 Nowatzki, Tony, author
書名 Optimization and mathematical modeling in computer architecture / Tony Nowatzki, Michael Ferris, Karthikeyan Sankaralingam, Cristian Estan, Nilay Vaish, David Wood
出版項 [San Rafael, California] : Morgan & Claypool Publishers, [2014]
©2014
國際標準書號 9781627052108 (ebook)
1627052100 (ebook)
國際標準號碼 10.2200/S00531ED1V01Y201308CAC026
book jacket
說明 1 online resource (xiii, 144 pages) : illustrations
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
系列 Synthesis lectures on computer architecture, 1935-3243 ; #26
Synthesis lectures in computer architecture ; #26. 1935-3235
附註 In the last few decades computer systems and the underlying hardware have steadily become larger and more complex. The need to increase their efficiency through architectural innovation has not abated, but quantitatively evaluating the effect of various choices has become more difficult. Performance and resource consumption are determined by complex interactions between many modules, each with many possible alternative implementations. We need powerful computer programs to explore large design spaces, but the traditional approach of developing simulators, building prototypes, or writing heuristic-based algorithms in traditional programming languages is often tedious and slow
Includes bibliographical references (pages 127-142)
1.1 Why this book? -- 1.1.1 Evolution of mathematical theories and algorithms -- 1.1.2 Maturity of solvers and modeling systems -- 1.1.3 Complexity of computer systems -- 1.2 Who is this book for? -- 1.3 What is this book about? -- 1.3.1 Mathematical modeling -- 1.3.2 Optimization as a modeling technique -- 1.3.3 The essential primitives of MILP -- 1.3.4 Illustrative examples -- 1.3.5 Benefits of modeling and MILP -- 1.4 What this book is not about -- 1.5 Book overview -- 1.6 Code provided with this book --
2. An overview of optimization -- 2.1 Overview of optimization -- 2.2 Models for optimization -- 2.2.1 Linear programming -- 2.2.2 Convex programming -- 2.2.3 Network flow problems -- 2.2.4 Mixed integer linear programming -- 2.2.5 Mixed integer nonlinear programs -- 2.3 Modeling problems as MILP -- 2.3.1 Logic and binary variables -- 2.3.2 Constraint logic programming -- 2.3.3 Ordering -- 2.3.4 Piecewise-linear models -- 2.3.5 Modeling mixed integer nonlinear programs -- 2.4 Solution methods -- 2.4.1 Branch-and-bound -- 2.4.2 Extensions to basic branch-and-bound -- 2.4.3 Column generation -- 2.4.4 Bender's decomposition -- 2.4.5 Other approaches -- 2.4.6 Modeling languages -- 2.5 Conclusion --
3. Case study: instruction set customization -- 3.1 Introduction -- 3.2 Overview -- 3.3 Formulation: parameters and decision variables -- 3.4 Formulation: constraints -- 3.5 Formulation: objective -- 3.6 Modeling limitations -- 3.7 Evaluation -- 3.7.1 Methodology -- 3.7.2 Results -- 3.8 Related work -- 3.9 Conclusions --
4. Case study: data center resource management -- 4.1 Introduction -- 4.2 Overview -- 4.3 Formulation: parameters and decision variables -- 4.4 Formulation: constraints -- 4.5 Formulation: objective -- 4.6 Modeling limitations -- 4.7 Evaluation -- 4.7.1 Methodology -- 4.7.2 Results -- 4.8 Related work -- 4.9 Conclusions --
5. Case study: spatial architecture scheduling -- 5.1 Introduction -- 5.2 Overview -- 5.3 Formulation: parameters and decision variables -- 5.4 Formulation: constraints -- 5.5 Formulation: objective -- 5.6 Architecture-specific modeling -- 5.6.1 Architecture-specific details for TRIPS -- 5.6.2 Architecture-specific details for DySER -- 5.6.3 Architecture-specific details for PLUG -- 5.7 Modeling limitations -- 5.8 Evaluation -- 5.8.1 Methodology -- 5.8.2 Results -- 5.9 Related work -- 5.10 Discussion and conclusions --
6. Case study: resource allocation in tiled architectures -- 6.1 Introduction -- 6.2 Overview -- 6.3 Formulation: parameters and decision variables -- 6.4 Formulation: constraints -- 6.5 Formulation: objective -- 6.6 Modeling limitations -- 6.7 Evaluation -- 6.7.1 Methodology -- 6.7.2 Results -- 6.8 Related work -- 6.9 Conclusions --
7. Conclusions -- 7.1 Properties of a MILP-friendly problem -- 7.2 Understanding the limitations of MILP -- 7.2.1 Properties of optimization problems unsuitable to MILP -- 7.2.2 Example problems poorly suited to MILP -- 7.3 Implementing your optimization problems in MILP -- 7.3.1 First steps -- 7.3.2 Dealing with MILP challenges -- 7.3.3 Optimizing and tuning models -- 7.4 Lessons learned
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Description based on print version record
鏈接 Print version: Optimization and mathematical modeling in computer architecture. [San Rafael, Calif.] : Morgan & Claypool Publishers, c2014 1627052097 (OCoLC)860882130
主題 Computer architecture -- Mathematical models
Mathematical optimization
Integer programming
Linear programming
COMPUTERS / Computer Literacy bisacsh
COMPUTERS / Computer Science bisacsh
COMPUTERS / Data Processing bisacsh
COMPUTERS / Hardware / General bisacsh
COMPUTERS / Information Technology bisacsh
COMPUTERS / Machine Theory bisacsh
COMPUTERS / Reference bisacsh
Integer Linear Programming
ILP
Mixed Integer Linear Programming
MILP
Mathematical Modeling
General Algebraic Modeling System
GAMS
Optimization
Spatial Architectures
Tiled Architectures
Scheduling
Resource Allocation
Instruction Set Customization
Electronic books
Alt Author Ferris, Michael C., author
Sankaralingam, Karthikeyan, author
Estan, Cristian, author
Vaish, Nilay, author
Wood, David Allen, author
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