Google XProf Adds Kernel-Level Profiling for TPU v7: Triple Buffering Boosts Pallas Matmul by 30%

1 reports archived0 independent sourcesupdated 10/10/2026, 14:52:35

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Google enhanced its open-source profiler XProf with kernel-level deep profiling for TPU v7 (Ironwood), enabling cycle-accurate hardware counter sampling for custom kernels like Pallas and Mosaic. The tool exposes the failure of static cost models when bypassing standard XLA passes, demonstrating that register-level insights guided triple-buffering optimizations to reduce matrix multiplication execution time by approximately 30%.

LATEST/Google enhanced its open-source profiler XProf with kernel-level deep profiling for TPU v7 (Ironwood), enabling cycle-accurate hardware counter sampling for custom kernels like Pallas and Mosaic. The tool exposes the failure of static cost models when bypassing standard XLA passes, demonstrating that register-level insights guided triple-buffering optimizations to reduce matrix multiplication execution time by approximately 30%.

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InfoQ 中文T2·Oct 10, 14:5268 pts
Google XProf Adds Kernel-Level Profiling for TPU v7: Triple Buffering Boosts Pallas Matmul by 30%
- XProf now supports runtime hardware counter sampling for custom Pallas/Mosaic kernels on TPU v7, revealing previously invisible HBM wait bottlenecks.
- Static cost models (e.g., FLOPs metrics) can be severely inaccurate because custom kernels bypass standard XLA passes; register-level real-time data is required.
- A case study showed that addressing memory stalls identified by the new tools via triple buffering reduced chunked matrix multiplication time from 125.5µs to 88µs (~30% improvement).
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