An iterations per second enhancement to Benchmark.
- benchmark/ips - benchmarks a blocks iterations/second. For short snippits of code, ips automatically figures out how many times to run the code to get interesting data. No more guessing at random iteration counts!
require 'benchmark/ips'
Benchmark.ips do |x|
# Configure the number of seconds used during
# the warmup phase (default 2) and calculation phase (default 5)
x.config(:time => 5, :warmup => 2)
# These parameters can also be configured this way
x.time = 5
x.warmup = 2
# Typical mode, runs the block as many times as it can
x.report("addition") { 1 + 2 }
# To reduce overhead, the number of iterations is passed in
# and the block must run the code the specific number of times.
# Used for when the workload is very small and any overhead
# introduces incorrectable errors.
x.report("addition2") do |times|
i = 0
while i < times
1 + 2
i += 1
end
end
# To reduce overhead even more, grafts the code given into
# the loop that performs the iterations internally to reduce
# overhead. Typically not needed, use the |times| form instead.
x.report("addition3", "1 + 2")
# Really long labels should be formatted correctly
x.report("addition-test-long-label") { 1 + 2 }
# Compare the iterations per second of the various reports!
x.compare!
endThis will generate the following report:
Calculating -------------------------------------
addition 71.254k i/100ms
addition2 68.658k i/100ms
addition3 83.079k i/100ms
addition-test-long-label
70.129k i/100ms
-------------------------------------------------
addition 4.955M (± 8.7%) i/s - 24.155M
addition2 24.011M (± 9.5%) i/s - 114.246M
addition3 23.958M (±10.1%) i/s - 115.064M
addition-test-long-label
5.014M (± 9.1%) i/s - 24.545M
Comparison:
addition2: 24011974.8 i/s
addition3: 23958619.8 i/s - 1.00x slower
addition-test-long-label: 5014756.0 i/s - 4.79x slower
addition: 4955278.9 i/s - 4.85x slower
Benchmark/ips will report the number of iterations per second for a given block of code. When analyzing the results, notice the percent of standard deviation which tells us how spread out our measurements are from the average. A high standard deviation could indicate the results having too much variability.
One benefit to using this method is benchmark-ips automatically determines the data points for testing our code, so we can focus on the results instead of guessing iteration counts as we do with the traditional Benchmark library.
Pass a custom suite to disable garbage collection during benchmark:
require 'benchmark/ips'
# Enable and start GC before each job run. Disable GC afterwards.
#
# Inspired by https://www.omniref.com/ruby/2.2.1/symbols/Benchmark/bm?#annotation=4095926&line=182
class GCSuite
def warming(*)
run_gc
end
def running(*)
run_gc
end
def warmup_stats(*)
end
def add_report(*)
end
private
def run_gc
GC.enable
GC.start
GC.disable
end
end
suite = GCSuite.new
Benchmark.ips do |x|
x.config(:suite => suite)
x.report("job1") { ... }
x.report("job2") { ... }
endIf you are comparing multiple implementations of a piece of code you may want
to benchmark them in separate invocations of Ruby so that the measurements
are independent of each other. You can do this with the hold! command.
Benchmark.ips do |x|
# Hold results between multiple invocations of Ruby
x.hold! 'filename'
endThis will run only one benchmarks each time you run the command, storing results in the specified file. The file is deleted when all results have been gathered and the report is shown.
Alternatively, if you prefer a different approach, the save! command is
available. Examples for hold! and save! are available in
the examples/ directory.
In some cases you may want to run multiple iterations of the warmup and calculation stages and take only the last result for comparison. This is useful if you are benchmarking with an implementation of Ruby that optimizes using tracing or on-stack-replacement, because to those implementations the calculation phase may appear as new, unoptimized code.
You can do this with the iterations option, which by default is 1. The
total time spent will then be iterations * warmup + iterations * time seconds.
Benchmark.ips do |x|
x.config(:iterations => 3)
# or
x.iterations = 3
endIf you want to share quickly your benchmark result with others. Run you benchmark
with SHARE=1 argument. I.e.: SHARE=1 ruby my_benchmark.rb.
Result will be sent to benchmark.fyi and benchmark-ips
will display the link to share the benchmark's result.
By default, the margin of error shown is plus-minus one standard deviation. If a more advanced statistical test is wanted, a bootstrap confidence interval can be calculated instead. A bootstrap confidence interval has the advantages of arguably being more mathematically sound for this application than a standard deviation, it additionally produces an error for relative slowdowns, which the standard deviation does not, and it is arguably more intuitive and actionable.
When a bootstrap confidence interval is used, a median of the interval is used rather than the mean of the samples, which is what you get with the default standard deviation.
The bootstrap confidence interval used is the one described by Tomas Kalibera. Note that for this technique to be valid your benchmark should have reached a non-periodic steady state with statistically independent samples (it should have warmed up) by the time measurements start.
Using a bootstrap confidence internal requires that the 'kalibera' gem is installed separately. This gem is not a formal dependency, as by default it is not needed.
gem install kalibera
Benchmark.ips do |x|
# The default is :stats => :sd, which doesn't have a configurable confidence
x.config(:stats => :bootstrap, :confidence => 95)
# or
x.stats = :bootstrap
x.confidence = 95
# confidence is 95% by default, so it can be omitted
end- None!
$ gem install benchmark-ips
After checking out the source, run:
$ rake newb
This task will install any missing dependencies, run the tests/specs, and generate the RDoc.
(The MIT License)
Copyright (c) 2015 Evan Phoenix
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