bisect-perf-regression

To find the exact commit that introduced a GPU kernel performance regression, you can automate a git bisect across a given commit range using a benchmark command. The process checks out commits, executes repeated benchmark runs with median aggregation, and reports the offending commit with its diff.

Automating git bisect for benchmark testing involves executing a user-provided benchmark command on each candidate commit to extract a numeric performance metric. The automated process applies threshold-based classification to identify regressions, optionally running build steps and stashing environment changes safely.

Yes, you can locate GPU kernel regressions with a build step by specifying optional build commands during the automated git bisect. The process safely handles environment-safe stashing and executes the required build before running repeated benchmark checks on each commit.

Yes, automated regression reporting includes the code diff for the offending commit. The generated comprehensive report identifies the exact commit that introduced the regression and provides a root-cause hypothesis based on the benchmark results.

Performance regressions during an automated git bisect are classified using threshold-based criteria applied to a numeric performance metric. The process extracts this metric by executing a user-provided benchmark command and aggregating multiple benchmark runs using the median.

You need automated git bisect for GPU kernel performance monitoring when debugging continuous integration pipelines or identifying the exact commit that introduces a performance regression. It applies to any codebase with GPU kernels where automated benchmarking is possible.