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In this book we give an overview of modeling techniques used to
describe computer systems to mathematical optimization tools. We
give a brief introduction to various classes of mathematical
optimization frameworks with special focus on mixed integer linear
programming which provides a good balance between solver time and
expressiveness. We present four detailed case studies --
instruction set customization, data center resource management,
spatial architecture scheduling, and resource allocation in tiled
architectures -- showing how MILP can be used and quantifying by
how much it outperforms traditional design exploration techniques.
This book should help a skilled systems designer to learn
techniques for using MILP in their problems, and the skilled
optimization expert to understand the types of computer systems
problems that MILP can be applied to.
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