# Benchmarking `factor`

## Microbenchmarking deterministic functions

We currently use [`criterion`] to benchmark deterministic functions,
such as `gcd` and `table::factor`.

Those benchmarks can be simply executed with `cargo bench` as usual,
but may require a recent version of Rust, *i.e.* the project's minimum
supported version of Rust does not apply to the benchmarks.


However, µbenchmarks are by nature unstable: not only are they specific to
the hardware, operating system version, etc., but they are noisy and affected
by other tasks on the system (browser, compile jobs, etc.), which can cause
`criterion` to report spurious performance improvements and regressions.

This can be mitigated by getting as close to [idealised conditions][lemire]
as possible:
- minimize the amount of computation and I/O running concurrently to the
  benchmark, *i.e.* close your browser and IM clients, don't compile at the
  same time, etc. ;
- ensure the CPU's [frequency stays constant] during the benchmark ;
- [isolate a **physical** core], set it to `nohz_full`, and pin the benchmark
  to it, so it won't be preempted in the middle of a measurement ;
- disable ASLR by running `setarch -R cargo bench`, so we can compare results
  across multiple executions.  


[`criterion`]: https://bheisler.github.io/criterion.rs/book/index.html
[lemire]: https://lemire.me/blog/2018/01/16/microbenchmarking-calls-for-idealized-conditions/
[isolate a **physical** core]: https://pyperf.readthedocs.io/en/latest/system.html#isolate-cpus-on-linux
[frequency stays constant]: XXXTODO


### Guidance for designing µbenchmarks

*Note:* this guidance is specific to `factor` and takes its application domain
into account; do not expect it to generalise to other projects.  It is based
on Daniel Lemire's [*Microbenchmarking calls for idealized conditions*][lemire],
which I recommend reading if you want to add benchmarks to `factor`.

1. Select a small, self-contained, deterministic component  
   `gcd` and `table::factor` are good example of such:
   - no I/O or access to external data structures ;
   - no call into other components ;
   - behaviour is deterministic: no RNG, no concurrency, ... ;
   - the test's body is *fast* (~100ns for `gcd`, ~10µs for `factor::table`),
     so each sample takes a very short time, minimizing variability and
     maximizing the numbers of samples we can take in a given time.

2. Benchmarks are immutable (once merged in `uutils`)  
   Modifying a benchmark means previously-collected values cannot meaningfully
   be compared, silently giving nonsensical results.  If you must modify an
   existing benchmark, rename it.

3. Test common cases  
   We are interested in overall performance, rather than specific edge-cases;
   use **reproducibly-randomised inputs**, sampling from either all possible
   input values or some subset of interest.

4. Use [`criterion`], `criterion::black_box`, ...  
   `criterion` isn't perfect, but it is also much better than ad-hoc
   solutions in each benchmark.


## Wishlist

### Configurable statistical estimators

`criterion` always uses the arithmetic average as estimator; in µbenchmarks,
where the code under test is fully deterministic and the measurements are
subject to additive, positive noise, [the minimum is more appropriate][lemire].


### CI & reproducible performance testing

Measuring performance on real hardware is important, as it relates directly
to what users of `factor` experience; however, such measurements are subject
to the constraints of the real-world, and aren't perfectly reproducible.
Moreover, the mitigations for it (described above) aren't achievable in
virtualized, multi-tenant environments such as CI.

Instead, we could run the µbenchmarks in a simulated CPU with [`cachegrind`],
measure execution “time” in that model (in CI), and use it to detect and report
performance improvements and regressions.

[`iai`] is an implementation of this idea for Rust.

[`cachegrind`]: https://www.valgrind.org/docs/manual/cg-manual.html
[`iai`]: https://bheisler.github.io/criterion.rs/book/iai/iai.html


### Comparing randomised implementations across multiple inputs

`factor` is a challenging target for system benchmarks as it combines two
characteristics:

1. integer factoring algorithms are randomised, with large variance in
   execution time ;

2. various inputs also have large differences in factoring time, that
   corresponds to no natural, linear ordering of the inputs.


If (1) was untrue (i.e. if execution time wasn't random), we could faithfully
compare 2 implementations (2 successive versions, or `uutils` and GNU) using
a scatter plot, where each axis corresponds to the perf. of one implementation.

Similarly, without (2) we could plot numbers on the X axis and their factoring
time on the Y axis, using multiple lines for various quantiles.  The large
differences in factoring times for successive numbers, mean that such a plot
would be unreadable.