Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (pages 127-142).

1. Introduction -- 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 --

Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

INSPEC

Google scholar

Google book search

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.

Also available in print.

Title from PDF title page (viewed on October 16, 2013).