PERFORMANCE Signal 198
How to use Google microbenchmarks for evaluating TPU performance
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Engineers working with TPUs can now empirically measure and isolate performance bottlenecks in specific subsystems like interconnect, compute, memory, and I/O rather than relying on vendor specifications alone. This enables systematic optimization and helps verify whether deployed hardware actually meets its theoretical performance claims.
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The suite measures five core areas—Network, Compute, HBM, Host Transfer, and Ragged-Paged Attention—each with specific metrics such as TFLOPs, MFU, GB/s, and latency.
The open-source accelerator-microbenchmarks repository includes configurable parameters for data types (bf16, fp8, fp32), buffer sizes, and iteration counts to tune tests to specific workloads.
Results can establish a Speed-of-Light baseline and build a Roofline model, enabling engineers to pinpoint whether bottlenecks stem from VMEM locality, interconnect efficiency, or other architectural factors.
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