`std::vector::const_iterator` is now automatically conformed to `UnsafeCxxRandomAccessIterator`, and `std::vector` is conformed to `CxxRandomAccessCollection`.
The manually added conformances are now redundant.
* Added a benchmark for KeyPaths where trivially-typed memory is preceded by non-trivially-typed memory.
* Reduces the workload of run_KeyPathClassStructs by a factor of 4.
The reported time was 1847 us.
The Swift benchmarking harness now has two distinct output formats:
* Default: Formatted text that's intended for human consumption.
Right now, this is just the minimum value, but we can augment that.
* `--json`: each output line is a JSON-encoded object that contains raw data
This information is intended for use by python scripts that aggregate
or compare multiple independent tests.
Previously, we tried to use the same output for both purposes. This required
the python scripts to do more complex parsing of textual layouts, and also meant
that the python scripts had only summary data to work with instead of full raw
sample information. This in turn made it almost impossible to derive meaningful
comparisons between runs or to aggregate multiple runs.
Typical output in the new JSON format looks like this:
```
{"number":89, "name":"PerfTest", "samples":[1.23, 2.35], "max_rss":16384}
{"number":91, "name":"OtherTest", "samples":[14.8, 19.7]}
```
This format is easy to parse in Python. Just iterate over
lines and decode each one separately. Also note that the
optional fields (`"max_rss"` above) are trivial to handle:
```
import json
for l in lines:
j = json.loads(l)
# Default 0 if not present
max_rss = j.get("max_rss", 0)
```
Note the `"samples"` array includes the runtime for each individual run.
Because optional fields are so much easier to handle in this form, I reworked
the Python logic to translate old formats into this JSON format for more
uniformity. Hopefully, we can simplify the code in a year or so by stripping
out the old log formats entirely, along with some of the redundant statistical
calculations. In particular, the python logic still makes an effort to preserve
mean, median, max, min, stdev, and other statistical data whenever the full set
of samples is not present. Once we've gotten to a point where we're always
keeping full samples, we can compute any such information on the fly as needed,
eliminating the need to record it.
This is a pretty big rearchitecture of the core benchmarking logic. In order to
try to keep things a bit more manageable, I have not taken this opportunity to
replace any of the actual statistics used in the higher level code or to change
how the actual samples are measured. (But I expect this rearchitecture will make
such changes simpler.) In particular, this should not actually change any
benchmark results.
For the future, please keep this general principle in mind: Statistical
summaries (averages, medians, etc) should as a rule be computed for immediate
output and rarely if ever stored or used as input for other processing. Instead,
aim to store and transfer raw data from which statistics can be recomputed as
necessary.
The `getFalse` utility function was not excluded from cross-module-optimization, which led the optimizer to completely eliminate the main loop body
Also, the passed `N` was shadowed by a local `n`.
