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.
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 knowGoogle adds Kernel Profiling suite to XProf open-source profiler
Enables cycle-level visibility into custom Pallas kernels
Previously kernels appeared as opaque blocks in trace captures
Reduces blind spots in TPU workload optimization
Google XProf adds cycle-level kernel profiling
Pallas custom kernels now visible at granular level (previously opaque blocks in traces)
Open-source TPU profiler enhancement
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