February 2026
Intermediate to advanced
384 pages
12h 48m
English
book
AI Data Center Network Design and Technologies
by Mahesh Subramaniam, Michal Styszynski, Himanshu Tambakuwala
Content preview from AI Data Center Network Design and Technologies
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What You’ll Find Inside
Chapter 1: Wonders in the Workload
Start your journey by examining the complete lifecycle of AI/ML workloads. This chapter explains how raw data is collected, labeled, and preprocessed before being routed into distributed training pipelines. You will understand the fundamentals of forward and backward propagation, gradient descent, and iterative optimization—and see how these processes scale across thousands of GPUs through data, pipeline, and tensor parallelism. The chapter introduces job completion time (JCT) and tail latency as key metrics and discusses how RDMA (in RoCEv2) facilitates the low-latency, high-throughput transfers needed for modern AI. The technical groundwork clarifies why lossless, high-radix, dynamically ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.