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sha256=d3dc8e3eab6108abe54e5976cac50697e4fb26d5c619723b210c3509e116e0a7
CHANGES.md.html
113.24.00
Switched to ppx.
Minor adjustments to the command line of profiler_tool.exe:
Make '-%' an alias for '-percentile'
Make '-percentile' accept a comma-separated list of numbers
Add '-median' argument that is equivalent to '-percentile 50'
113.00.00
Changed delta timers and probes so they record the total amount of time/value
change between each start and pause.
112.19.00
Initial release
112.17.00
Initial release
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 thatCore_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
(theBENCH
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 functionf
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 runningf
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.