How to use Google microbenchmarks for evaluating TPU performance

Understanding the performance of Tensor Processing Units (TPUs) requires empirical evaluations beyond theoretical specifications, utilizing a microbenchmark suite to assess real-world performance across various architectural environments and workloads. This suite evaluates TPUs by segmenting performance into key functional areas such as network, compute, high-bandwidth memory, host transfer, and attention mechanisms, providing granular insights into whether the devices meet their theoretical capabilities and identifying architecture-specific bottlenecks. By establishing a "Speed-of-Light" baseline, these microbenchmarks transform performance optimization into an empirical discipline, using the Roofline model to classify bottlenecks as compute-bound, memory-bound, or network-bound, and guiding optimization strategies like kernel selection, sharding, and rematerialization. The insights gained from these benchmarks are crucial for predictive modeling and large-scale deployment optimization, as demonstrated in a case study on Ironwood TPU 7x, where microbenchmark data significantly improved the performance of a Mixture-of-Experts training workload.

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