Add dependency-free CPU-bound workloads adapted from pyperformance 1.14.0 to cover interpreter, standard-library serialization, regex, numeric, and garbage-collection paths under the existing Criterion and CodSpeed harnesses. Keep inputs deterministic and scale expensive cases for simulation while checking that benchmark execution does not leak interpreter state. Assisted-by: Codex:gpt-5.6-sol
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Benchmarking
These are some files to determine performance of rustpython.
Usage
Running cargo bench from the root of the repository will start the benchmarks. Once done there will be a graphical
report under target/criterion/report/index.html that you can use use to view the results.
cargo bench supports name matching to run a subset of the benchmarks. To
run only the sorted microbenchmark, you can run:
cargo bench sorted
To view Python tracebacks during benchmarks, run RUST_BACKTRACE=1 cargo bench. You can also bench against a
specific installed Python version by running:
PYTHON_SYS_EXECUTABLE=python3.13 cargo bench
Continuous benchmarking with CodSpeed
The benchmarks are also run on every pull request by
CodSpeed, which measures them with CPU simulation
instead of wall time. criterion is aliased to codspeed-criterion-compat, so nothing changes for
cargo bench: the compatibility layer only takes over when the CodSpeed runner drives the
benchmarks.
To reproduce a CodSpeed run locally, install cargo-codspeed
and the CodSpeed CLI, then run:
cargo codspeed build -p rustpython -p rustpython-sre_engine
codspeed run --mode simulation -- cargo codspeed run -p rustpython -p rustpython-sre_engine
Two things differ when the benchmarks run under CodSpeed:
- The CPython comparison benchmarks are skipped. They are useful to compare RustPython against CPython locally, but CodSpeed tracks the evolution of RustPython itself, and running them would double the duration of an already slow instrumented run.
- The microbenchmarks using
ITERATIONSrun with a single value instead of five. The criterion benchmark id does not include the iteration count, so all five sizes are reported under the same name.
Adding a benchmark
Simply adding a file to the benchmarks/ directory will add it to the set of files benchmarked. Each file is tested
in two ways:
- The time to parse the file to AST
- The time it takes to execute the file
Adding a micro benchmark
Micro benchmarks are small snippets of code added under the microbenchmarks/ directory. A microbenchmark file has
two sections:
- Optional setup code
- The code to be benchmarked
These two sections are delimited by # ---. For example:
a_list = [1,2,3]
# ---
len(a_list)
Only len(a_list) will be timed. Setup or benchmarked code can optionally reference a variable called ITERATIONS. If
present then the benchmark code will be invoked 5 times with ITERATIONS set to a value between 100 and 1,000. For
example:
obj = [i for i in range(ITERATIONS)]
ITERATIONS can appear in both the setup code and the benchmark code.
MacOS setup
On MacOS you will need to add the following to a .cargo/config file:
[target.x86_64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
]
Benchmark source
- https://benchmarksgame-team.pages.debian.net/benchmarksgame/program/nbody-python3-2.html
- The workloads marked as adapted from pyperformance come from
python/pyperformance 1.14.0.
Their
pyperfrunners and internal timers are removed so Criterion is the only measurement harness, and some input sizes are reduced for CodSpeed simulation.