package core_bench
Install
Dune Dependency
Authors
Maintainers
Sources
sha256=a8e8804bb6dc6f52e82231570f6594eacd3b6732eaa18dfe1e1e03333b512a35
md5=24b1327b11adb200a4cc87298a335c22
CHANGES.md.html
113.24.00
Switched to ppx.
112.35.00
Exposed the equality of
Core_bench.Std.Bench.Test.t
withCore_bench.Test.t
, so that one can get the name of a test.This is useful for filtering based on test name.
112.17.00
Updated code to follow some core changes
112.06.00
Solved a problem in which OCaml 4.02 was optimizing away benchmarks, making them meaningless.
112.01.00
fixed legacy format string
109.58.00
Added support for saving inline benchmark measurements to tabular files for easy loading into Octave.
109.55.00
Improved
bench.mli
's generated docs and added some usage examples.This also partly satisfies issue #3.
Added the ability to create groups of benchmarks with a common prefix.
For example, the prefix "Perf" below is created in created using
create_group
:let command = Bench.make_command [ Bench.Test.create ~name:"Time.now" (fun () -> ignore (Time.now ())); ... Bench.Test.create_group ~name:"Perf" [ Bench.Test.create ~name:"TSC.now" ...
and the output shows:
Estimated testing time 7s (7 benchmarks x 1s). Change using -quota SECS. ┌───────────────────────────────────────────┬──────────┬─────────┬────────────┐ │ Name │ Time/Run │ mWd/Run │ Percentage │ ├───────────────────────────────────────────┼──────────┼─────────┼────────────┤ │ Time.now │ 41.38ns │ 2.00w │ 16.72% │ │ Calibrator.calibrate │ 247.42ns │ 32.00w │ 100.00% │ │ Perf/TSC.now │ 7.84ns │ │ 3.17% │ │ Perf/TSC.to_time │ 9.35ns │ 2.00w │ 3.78% │ │ Perf/TSC.to_time (TSC.now ()) │ 13.22ns │ 2.00w │ 5.34% │ │ Perf/TSC.to_nanos_since_epoch │ 10.83ns │ │ 4.38% │ │ Perf/TSC.to_nanos_since_epoch(TSC.now ()) │ 14.86ns │ │ 6.00% │ └───────────────────────────────────────────┴──────────┴─────────┴────────────┘
109.53.00
Fixed a bug in
Core_bench
where the linear regression was sometimes supplied with spurious data.This showed up when doing custom regressions that allow for a non-zero y-intercept.
109.52.00
Exposed an extensible form of
make_command
so that inline-benchmarking and the other tools can add more commandline flags.A significant rewrite of
Core_bench
.The rewrite provides largely the same functionality as the older version. The most visible external change is that the API makes it clear that
Core_bench
performs linear regressions to come up with its numbers. Further, it allows running user-specified multivariate regressions in addition to the built in ones.The underlying code has been cleaned up in many ways, some of which are aimed at improving the implementation of inline benchmarking (the
BENCH
syntax, which has not yet been released).
109.41.00
Columns that have a
+
prefix are now always displayed, whereas columns that don't are displayed only if they have meaningful data.Added the ability to reload saved metrics (benchmark test data) so that bench can re-analyze them.
109.39.00
Added support for additional predictors like minor/major GCs and compactions, using multi-variable linear regression.
Replaced linear regression with multi-variable linear regression. The original algorithm estimated the cost of a function
f
by using a linear regression of the time taken to runf
vs the number of runs. The new version adds the ability to include additional predictors such as minor GCs, compactions etc.This allows a more fine-grained split-up of the running costs of a function, distinguishing between the time spent actually running
f
and the time spent doing minor GCs, major GCs or compactions.Added a forking option that allows benchmarks to be run in separate processes.
This avoids any influence (e.g. polluting the cache, size of live heap words) they might otherwise have on each other.
109.32.00
Changed
-save
to output compaction information.Added indexed tests.
These are benchmarks of the form
int -> unit -> unit
, which can be profiled for a list of user specifiedint
s.
109.30.00
Report compaction stats
109.27.00
Added R^2 error estimation.
Adding this metric should give us a sense of how closely the given values fit a line. Even dots that are fairly scattered can give tight confidence intervals. We would like to have to number to have a sense of how much noise we have.