How to Use Google Microbenchmarks for Evaluating TPU Performance

Google presents an open-source TPU microbenchmark suite that measures network, compute, high-bandwidth memory, host-transfer, and attention performance independently. The resulting empirical measurements can be used to construct a Roofline model and determine whether a machine learning workload is constrained by arithmetic throughput, memory bandwidth, or interconnect behavior. That diagnosis can guide targeted changes such as kernel tuning, mesh sharding, and rematerialization instead of relying only on end-to-end model throughput.