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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:
Ziyu Shao is 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 Deng is 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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