tt-lang-profile-optimize

To profile TT-Lang kernels, you can use auto-profiling and signpost-based measurement to locate performance hotspots. This requires configuring TT-Metal tooling environment variables like TTLANG_AUTO_PROFILE and TTLANG_SIGNPOST_PROFILE to properly instrument and collect profiling data.

TT-Lang auto-profiling requires setting TT_METAL_HOME, TT_METAL_DEVICE_PROFILER, TTLANG_AUTO_PROFILE, TTLANG_SIGNPOST_PROFILE, and TTLANG_PERF_DUMP. These variables configure the TT-Metal tooling needed to instrument kernels and collect profiling data.

To generate a perf summary for a slow TT-Lang kernel, run the profiler on the kernel file to establish baseline performance data. This utilizes the auto-profiling workflow to structure an end-to-end optimization cycle.

Yes, signposts can be used for performance measurement to locate hotspots within TT-Lang kernels. By enabling TTLANG_SIGNPOST_PROFILE, you can instrument specific code sections and collect targeted profiling data.

The best way to optimize TT-Lang kernels is following an end-to-end optimization workflow: instrument the kernel with auto-profiling, analyze the perf summary to find bottlenecks, and apply targeted tuning until the target throughput is met.

Yes, TT-Lang profiling can be applied during both development and production workloads. It focuses on auto-profiling, perf summaries, and signposts to continuously identify bottlenecks and apply targeted performance improvements.