Topology Design for Large-Scale Interconnection Networks: A Modern Approach

Topology Design for Large-Scale Interconnection Networks: A Modern Approach book cover

Topology Design for Large-Scale Interconnection Networks: A Modern Approach

Author(s): Ziyu Shao (Author), Yichi Deng (Author)

  • Publisher Finelybook 出版社: Wiley
  • Publication Date 出版日期: August 10, 2026
  • Edition 版本: 1st
  • Language 语言: English
  • Print length 页数: 400 pages
  • ISBN-10: 139436251X
  • ISBN-13: 9781394362516

Book Description

Transforming Network Topology Design from Art to Science

For decades, the design of large-scale network topologies has relied heavily on heuristic intuition and bottom-up engineering experience. While classic structures like Fat-tree have achieved immense success, the underlying mathematical principles governing their performance have remained largely implicit. This book introduces the first general framework capable of systematically generating and optimizing large-scale network topologies through a rigorous top-down approach. It reveals the hidden mathematical principles behind successful engineering designs, using Ramanujan graphs and combinatorial designs to achieve the optimal trade-off among various performance metrics. From clarifying the success of classic Fat-tree topology to designing future-proof AI clusters, this framework provides a universal blueprint for next-generation interconnection networks.

It is an essential guide for researchers, engineers, industry professionals and students seeking to master the topology design and analysis of interconnection networks.

Topics explored in this book include:

  • Mathematical background of topology design, including basic graph theory, algebra graph theory and combinatorial design
  • A unified topology design flow, including intra-module design, connection design, inter-module design, and finalization
  • Reinterpretation and evolution of several representative topology design instances, including families of fat-tree, HyperX, Dcell, and Bcube
  • The design method of brand-new topologies from scratch and guiding principles for the selection of design patterns and parameters
  • Applications to intelligent computing center (AI Cluster) and other emerging scenarios
  • Future directions of topology design, including both theoretical exploration and practical extension

Editorial Reviews

Editorial Reviews

From the Back Cover

Transforming Network Topology Design from Art to Science

For decades, the design of large-scale network topologies has relied heavily on heuristic intuition and bottom-up engineering experience. While classic structures like Fat-tree have achieved immense success, the underlying mathematical principles governing their performance have remained largely implicit. This book introduces the first general framework capable of systematically generating and optimizing large-scale network topologies through a rigorous top-down approach. It reveals the hidden mathematical principles behind successful engineering designs, using Ramanujan graphs and combinatorial designs to achieve the optimal trade-off among various performance metrics. From clarifying the success of classic Fat-tree topology to designing future-proof AI clusters, this framework provides a universal blueprint for next-generation interconnection networks.

It is an essential guide for researchers, engineers, industry professionals and students seeking to master the topology design and analysis of interconnection networks.

Topics explored in this book include:

  • Mathematical background of topology design, including basic graph theory, algebra graph theory and combinatorial design
  • A unified topology design flow, including intra-module design, connection design, inter-module design, and finalization
  • Reinterpretation and evolution of several representative topology design instances, including families of fat-tree, HyperX, Dcell, and Bcube
  • The design method of brand-new topologies from scratch and guiding principles for the selection of design patterns and parameters
  • Applications to intelligent computing center (AI Cluster) and other emerging scenarios
  • Future directions of topology design, including both theoretical exploration and practical extension

About the Author

ZIYU SHAOis a Professor and the Director of the Network Intelligence Center at the School of Information Science and Technology, ShanghaiTech University, Shanghai, China. He was a Visiting Research Fellow with the EE department at Princeton University; a Visiting Professor at the EECS department, University of California at Berkeley; and a Visiting Scientist at the Simons Institute for the Theory of Computing at Berkeley.

YICHENG DENGis a Postgraduate Student at ShanghaiTech University, studying under the supervision of Dr. Ziyu Shao. She is a student member of the IEEE.

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