python / python/cpython

Using the unit tests as the PGO task has problems

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build performance type-feature
Lingua principale
Python
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77.2k
Fork
35.9k
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Descrizione

Feature or enhancement

Proposal:

When Python is compiled with --enabled-optimizations, which turns on PGO (program guided optimizations), the build will run a subset of the unit tests as the "task" to generate profile information. Included in the profile is information like counts of the taken side of a CPU conditional branch instructions. To get the best optimization, your PGO task should match the branch taken behavior of your real workloads.

Using the unit tests has the advantage that we have good code coverage in terms of executing most branches and code paths. It also has the advantage of being available without any external dependencies. It has the disadvantage that the code executed during unit tests is likely quite atypical of what's executed during real applications. Running the ./python -X perf -m test --pgo under the "perf" tool, I see the following results:

Children Self Symbol
97.39% 21.16% _PyEval_EvalFrameDefault
34.37% 2.39% deduce_unreachable
24.82% 1.50% _PyGC_Collect
21.11% 1.32% gc_collect_region
20.95% 0.00% gc_collect
19.50% 0.00% py::gc_collect:/home/nas/src/cpython/Lib/test/support/init.py
14.98% 0.54% PyObject_Vectorcall
10.95% 1.48% dict_traverse
7.57% 0.00% _PyPegen_run_parser_from_string
7.46% 0.00% _PyPegen_run_parser
7.46% 0.00% _PyPegen_parse
7.33% 0.24% py::_make_iterencode.._iterencode_dict:/home/nas/src/cpython/Lib/json/encoder.py
7.18% 7.09% visit_reachable
6.43% 0.02% expression_rule
6.13% 0.00% PyRun_StringFlags
6.06% 1.90% _PyEval_Vector
5.99% 5.91% visit_decref
5.80% 0.00% builtin_eval
5.66% 0.04% disjunction_rule

This profile reveals a number of problems. First, a large fraction of time in spent in the cyclic GC. That's because the unit test framework calls test.support.gc_collect() before each test case. That function triggers three full GC collections. Other tests also call the GC explicitly. This is not behavior typical of a real program.

Also taking a lot of time are functions related to parsing and compiling Python code. Notice the builtin_eval() function, for example. I suspect that's mostly a result of using "doctest". Again, this would not be typical of real programs.

I think we should replace the PGO task with a program that more closely represents the behavior of real Python programs. There are at least two potential advantages: it could make the compiled Python binary faster for real programs, it could make our benchmark results less noisy since the compiler would be doing a better and most consistent job of generating optimal code.

Has this already been discussed elsewhere?

This is a minor feature, which does not need previous discussion elsewhere

Links to previous discussion of this feature:

No response

Linked PRs
  • gh-130702

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Direzione di ricerca

Inizia esaminando l'attività PGO attuale di --enable-optimizations e il comando ./python -X perf -m test --pgo, inclusi test.support.gc_collect in Lib/test/support/init.py e le osservazioni di profiling relative a Lib/json/encoder.py. Il lavoro sarà completato quando sarà stato definito e validato un workload sostitutivo rappresentativo, quindi il suo profilo e il suo impatto sui benchmark saranno stati confrontati con quelli dell'attività di unit test esistente.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
python
Ambito
build-system, performance
Tipo di issue
Funzionalità
Difficoltà
5/5
Tempo stimato
Più di una settimana
Stato di attività
Ferma
Chiarezza
Abbastanza chiara
Idoneità per principianti
35/100

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