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Google Adds Cycle-Level Kernel Profiling to XProf

Google's XProf just got cycle-level visibility into TPU kernels. For practitioners optimizing AI workloads on TPU, this closes a debugging blind spot.

Reporting by InfoQ AI/ML 1 min brief
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The KeyNews take

Why it matters

A developer-facing tooling improvement that directly enables better TPU utilization and kernel optimization. Practitioners building custom Pallas kernels can now see exactly where cycles are spent, shifting from guesswork to data-driven tuning.

The key facts

8 to know

  1. Google adds Kernel Profiling suite to XProf open-source profiler

  2. Enables cycle-level visibility into custom Pallas kernels

  3. Previously kernels appeared as opaque blocks in trace captures

  4. Reduces blind spots in TPU workload optimization

  5. Google XProf adds cycle-level kernel profiling

  6. Pallas custom kernels now visible at granular level (previously opaque blocks in traces)

  7. Open-source TPU profiler enhancement

  8. Enables developer optimization of TPU utilization

Go to the source

InfoQ AI/MLinfoq.com

Publisher excerpt: Google has added a Kernel Profiling suite to XProf. This is its open-source profiler for TPU workloads. Now, developers can see cycle-level details in custom Pallas kernels. Before, these kernels appeared as single opaque blocks in trace captures. By Claudio Masolo
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