e3b013047d
Perf was nice and flat after https://skia-review.googlesource.com/c/skia/+/83240 but there was a 4-5x slowdown on the benchmarks themselves, indicating that perhaps we were running nanobench on the LITTLE cores (now on powersave) instead of the big cores we thought (which were recently scaled down). This experiment will show us FOR CERTAIN that we are running on core 0 or 1 which is at .6 max frequency. We expect to see a speedup from the previous results. This is leading to turning of CPUs we don't need to make sure nanobench is running on the ones we expect. Bug: skia: NOTRY=true Change-Id: Ida65181e4d90e778e65e3f22d761288b9ade64f6 Reviewed-on: https://skia-review.googlesource.com/84201 Reviewed-by: Mike Klein <mtklein@chromium.org> Commit-Queue: Kevin Lubick <kjlubick@google.com> |
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|---|---|---|
| .. | ||
| bookmaker.expected | ||
| bundle_recipes.expected | ||
| calmbench.expected | ||
| check_generated_files.expected | ||
| compile.expected | ||
| ct_skps.expected | ||
| housekeeper.expected | ||
| infra.expected | ||
| perf.expected | ||
| recreate_skps.expected | ||
| skpbench.expected | ||
| test.expected | ||
| update_meta_config.expected | ||
| upload_calmbench_results.expected | ||
| upload_coverage_results.expected | ||
| upload_dm_results.expected | ||
| upload_nano_results.expected | ||
| upload_skiaserve.expected | ||
| bookmaker.py | ||
| bundle_recipes.py | ||
| calmbench.py | ||
| check_generated_files.py | ||
| compile.py | ||
| ct_skps.py | ||
| housekeeper.py | ||
| infra.py | ||
| perf.py | ||
| README.md | ||
| recreate_skps.py | ||
| skpbench.py | ||
| test.py | ||
| update_meta_config.py | ||
| upload_calmbench_results.py | ||
| upload_coverage_results.py | ||
| upload_dm_results.py | ||
| upload_nano_results.py | ||
| upload_skiaserve.py | ||
Skia Recipes
These are the top-level scripts which run inside of Swarming tasks to perform all of Skia's automated testing.
To run a recipe locally:
$ python infra/bots/recipes.py run --workdir=/tmp/<workdir> <recipe name without .py> key1=value1 key2=value2 ...
Each recipe may have its own required properties which must be entered as key/value pairs in the command.
When you change a recipe, you generally need to re-train the simulation test:
$ python infra/bots/recipes.py test run --train
Or:
$ cd infra/bots; make train
The test generates expectations files for the tests contained within each recipe which illustrate which steps would run, given a particular set of inputs. Pay attention to the diffs in these files when making changes to ensure that your change has the intended effect.