pyperf commands¶

Commands:

pyperf show
pyperf compare_to
pyperf stats
pyperf check
pyperf dump
pyperf hist
pyperf metadata
pyperf timeit
pyperf command
pyperf system
pyperf collect_metadata
pyperf slowest
pyperf convert

The Python pyperf module comes with a pyperf program which includes different commands. If for some reasons, pyperf program cannot be used, python3 -m pyperf ... can be used: it is the same, it’s just longer to type :-) For example, the -m pyperf ... syntax is preferred for timeit because this command uses the running Python program.

General note: if a filename is -, read the JSON content from stdin.

pyperf show¶

Show benchmarks of one or multiple benchmark suites:

python3 -m pyperf show
    [-q/--quiet]
    [-d/--dump]
    [-m/--metadata]
    |-g/--hist] [-t/--stats]
    [-b NAME/--benchmark NAME]
    filename.json [filename2.json ...]

--quiet enables the quiet mode
--dump displays the benchmark run results, see pyperf dump command
--metadata displays benchmark metadata: see pyperf metadata command
--hist renders an histogram of values, see pyperf hist command
--stats displays statistics (min, max, …), see pyperf stats command
--benchmark NAME only displays the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: The --benchmark option can now be specified multiple times.

Example:

$ python3 -m pyperf show telco.json
Mean +- std dev: 22.5 ms +- 0.2 ms

Example with metadata:

$ python3 -m pyperf show telco.json --metadata
Metadata:
- boot_time: 2016-10-19 01:10:08
- cpu_count: 4
- cpu_model_name: Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz
- description: Telco decimal benchmark
- hostname: selma
- loops: 8
- name: telco
- perf_version: 0.8.2
...

Mean +- std dev: 22.5 ms +- 0.2 ms

pyperf compare_to¶

Compare benchmark suites, use the first file as the reference:

python3 -m pyperf compare_to
    [-v/--verbose] [-q/--quiet]
    [-G/--group-by-speed]
    [--min-speed=MIN_SPEED]
    [--table]
    [--table-format=rest|md]
    [-b NAME/--benchmark NAME]
    reference.json changed.json [changed2.json ...]

Options:

--group-by-speed: group results by “Slower”, “Faster” and “Same speed”
--min-speed: Absolute minimum of speed in percent to consider that a benchmark is significant (default: 0%)
--table: Render a table.
--table-format: Table rendering format.
--benchmark NAME only displays the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: The --benchmark option can now be specified multiple times.

Changed in version 2.3: The --table-format option now can designate format between reST and markdown.

pyperf determines whether two samples differ significantly using a Student’s two-sample, two-tailed t-test with alpha equals to 0.95.

If the benchmark suites contain more than one benchmark, the geometric mean of benchmark results means normalized to the reference results means is computed. It is a convenient index to summarize benchmark suite results normalized to the reference suite. See How not to lie with statistics: the correct way to summarize benchmark results paper by Philip J. Fleming and John J. Wallace (ACM, 1986).

Example 1 comparing Python 3.8 to Python 3.6:

$ python3 -m pyperf compare_to py36.json py38.json
Mean +- std dev: [py36] 4.70 us +- 0.18 us -> [py38] 4.22 us +- 0.08 us: 1.11x faster

On this example, py36 is the reference: py38 is faster than py36 (4.22 us is less than 4.70 us).

Example 2 comparing two suites (Python 3.7 and Python 3.8) to a reference suite (Python 3.6):

$ python3 -m pyperf compare_to --table mult_list_py36.json mult_list_py37.json mult_list_py38.json
+----------------+----------------+-----------------------+-----------------------+
| Benchmark      | mult_list_py36 | mult_list_py37        | mult_list_py38        |
+================+================+=======================+=======================+
| [1]*1000       | 2.13 us        | 2.09 us: 1.02x faster | not significant       |
+----------------+----------------+-----------------------+-----------------------+
| [1,2]*1000     | 3.70 us        | 5.28 us: 1.42x slower | 3.18 us: 1.16x faster |
+----------------+----------------+-----------------------+-----------------------+
| [1,2,3]*1000   | 4.61 us        | 6.05 us: 1.31x slower | 4.17 us: 1.11x faster |
+----------------+----------------+-----------------------+-----------------------+
| Geometric mean | (ref)          | 1.22x slower          | 1.09x faster          |
+----------------+----------------+-----------------------+-----------------------+

On this example, mult_list_py36 (Python 3.6) is the reference. According to geometric mean, mult_list_py37 (Python 3.7) is slower than mult_list_py36, whereas mult_list_py38 (Python 3.8) is faster than mult_list_py36.

The geometric mean is a convenient index to summarize the 3 benchmark results of each suite as a single index which is normalized to the reference suite results. For example, mult_list_py37 is faster on one benchmark and slower on two others: according to the geometric mean, it is slower than the reference.

See also the --compare-to option of the Runner CLI.

pyperf stats¶

Compute statistics on a benchmark result:

python3 -m pyperf stats
    [-b NAME/--benchmark NAME]
    file.json [file2.json ...]

Options:

--benchmark NAME only displays the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: Count the number of outlier values. The --benchmark option can now be specified multiple times.

Computed values:

Mean and standard deviation: see Benchmark.mean() and Benchmark.stdev()
Median and median absolute deviation (MAD): see Benchmark.median() and Benchmark.median_abs_dev()
Percentiles: see Benchmark.percentile()
Outliers: number of values out of the range [Q1 - 1.5*IQR; Q3 + 1.5*IQR] where IQR stands for the interquartile range.

Example:

$ python3 -m pyperf stats telco.json
Total duration: 29.2 sec
Start date: 2016-10-21 03:14:19
End date: 2016-10-21 03:14:53
Raw value minimum: 177 ms
Raw value maximum: 183 ms

Number of calibration run: 1
Number of run with values: 40
Total number of run: 41

Number of warmup per run: 1
Number of value per run: 3
Loop iterations per value: 8
Total number of values: 120

Minimum:         22.1 ms
Median +- MAD:   22.5 ms +- 0.1 ms
Mean +- std dev: 22.5 ms +- 0.2 ms
Maximum:         22.9 ms

  0th percentile: 22.1 ms (-2% of the mean) -- minimum
  5th percentile: 22.3 ms (-1% of the mean)
 25th percentile: 22.4 ms (-1% of the mean) -- Q1
 50th percentile: 22.5 ms (-0% of the mean) -- median
 75th percentile: 22.7 ms (+1% of the mean) -- Q3
 95th percentile: 22.9 ms (+2% of the mean)
100th percentile: 22.9 ms (+2% of the mean) -- maximum

Number of outlier (out of 22.0 ms..23.0 ms): 0

Values:

Median
“std dev”: Standard deviation (standard error)

pyperf check¶

Check if benchmarks are stable:

python3 -m pyperf check
    [-b NAME/--benchmark NAME]
    filename [filename2 ...]

Options:

--benchmark NAME only check the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: The --benchmark option can now be specified multiple times.

Checks:

Warn if the standard deviation is greater than 10% of the mean
Warn if the minimum or the maximum is 50% smaller or greater than the mean
Warn if the shortest raw value took less than 1 millisecond
Warn if nohz_full Linux kernel option and the Linux intel_pstate CPU driver if found in the cpu_config metadata

Example of a stable benchmark:

$ python3 -m pyperf check telco.json
The benchmark seem to be stable

Example of an unstable benchmark:

$ python3 -m pyperf timeit -l1 -p3 '"abc".strip()' -o timeit_strip.json -q
Mean +- std dev: 750 ns +- 89 ns

$ python3 -m pyperf check timeit_strip.json
WARNING: the benchmark result may be unstable
* the standard deviation (89.4 ns) is 12% of the mean (750 ns)
* the shortest raw value is only 636 ns

Try to rerun the benchmark with more runs, values and/or loops.
Run 'python3 -m pyperf system tune' command to reduce the system jitter.
Use pyperf stats, pyperf dump and pyperf hist to analyze results.
Use --quiet option to hide these warnings.

pyperf dump¶

Display the benchmark run results:

python3 -m pyperf dump
    [-q/--quiet]
    [-v/--verbose]
    [--raw]
    [-b NAME/--benchmark NAME]
    file.json [file2.json ...]

Options:

--quiet enables the quiet mode: hide warmup values
--verbose enables the verbose mode: show run metadata
--raw displays raw values rather than values
--benchmark NAME only displays the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: The --benchmark option can now be specified multiple times.

Example:

$ python3 -m pyperf dump telco.json
Run 1: calibrate the number of loops: 8
- calibrate 1: 23.1 ms (loops: 1, raw: 23.1 ms)
- calibrate 2: 22.5 ms (loops: 2, raw: 45.0 ms)
- calibrate 3: 22.5 ms (loops: 4, raw: 89.9 ms)
- calibrate 4: 22.4 ms (loops: 8, raw: 179 ms)
Run 2: 1 warmup, 3 values, 8 loops
- warmup 1: 22.5 ms
- value 1: 22.8 ms
- value 2: 22.5 ms
- value 3: 22.6 ms
(...)
Run 41: 1 warmup, 3 values, 8 loops
- warmup 1: 22.5 ms
- value 1: 22.6 ms
- value 2: 22.4 ms
- value 3: 22.4 ms

Example in verbose mode:

$ python3 -m pyperf dump telco.json -v
Metadata:
  cpu_affinity: 2-3
  cpu_config: 2-3=driver:intel_pstate, intel_pstate:turbo, governor:performance, isolated; idle:intel_idle
  cpu_count: 4
  cpu_model_name: Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz
  hostname: selma
  loops: 8
  name: telco
  perf_version: 0.8.2
  ...

Run 1: calibrate the number of loops
- calibrate 1: 23.1 ms (loops: 1, raw: 23.1 ms)
- calibrate 2: 22.5 ms (loops: 2, raw: 45.0 ms)
- calibrate 3: 22.5 ms (loops: 4, raw: 89.9 ms)
- calibrate 4: 22.4 ms (loops: 8, raw: 179 ms)
- Metadata:
  cpu_freq: 2=3596 MHz, 3=1352 MHz
  cpu_temp: coretemp:Physical id 0=67 C, coretemp:Core 0=51 C, coretemp:Core 1=67 C
  date: 2016-10-21 03:14:19.670631
  duration: 338 ms
  load_avg_1min: 0.29
  ...
Run 2:
- warmup 1: 22.5 ms
- value 1: 22.8 ms
- value 2: 22.5 ms
- value 3: 22.6 ms
- Metadata:
  cpu_freq: 2=3596 MHz, 3=2998 MHz
  cpu_temp: coretemp:Physical id 0=67 C, coretemp:Core 0=51 C, coretemp:Core 1=67 C
  date: 2016-10-21 03:14:20.496710
  duration: 723 ms
  load_avg_1min: 0.29
  ...
...

pyperf hist¶

Render an histogram in text mode:

python3 -m pyperf hist
    [-n BINS/--bins=BINS] [--extend]
    [-b NAME/--benchmark NAME]
    filename.json [filename2.json ...]

--bins is the number of histogram bars. By default, it renders up to 25 bars, or less depending on the terminal size.
--extend: don’t limit to 80 columns x 25 lines but fill the whole terminal if it is wider.
--benchmark NAME only displays the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: The --benchmark option can now be specified multiple times.

If multiple files are used, the histogram is normalized on the minimum and maximum of all files to be able to easily compare them.

Example:

$ python3 -m pyperf hist telco.json
4 ms:  1 ##
4 ms:  1 ##
4 ms:  2 #####
5 ms:  1 ##
5 ms:  1 ##
5 ms:  4 #########
6 ms:  8 ###################
6 ms:  6 ##############
7 ms: 11 ##########################
7 ms: 13 ##############################
7 ms: 18 ##########################################
8 ms: 21 #################################################
8 ms: 34 ###############################################################################
8 ms: 26 ############################################################
9 ms: 11 ##########################
9 ms: 14 #################################
0 ms: 17 ########################################
0 ms: 14 #################################
0 ms: 10 #######################
1 ms: 10 #######################
1 ms:  7 ################
1 ms: 12 ############################
2 ms:  5 ############
2 ms:  2 #####
3 ms:  0 |
3 ms:  1 ##

See Gaussian function and Probability density function (PDF).

pyperf metadata¶

Display metadata of benchmark files:

python3 -m pyperf metadata
    [-b NAME/--benchmark NAME]
    filename [filename2 ...]

Options:

--benchmark NAME only displays the benchmark called NAME. The option can be specified multiple times.

Changed in version 1.2: The --benchmark option can now be specified multiple times.

Example:

$ python3 -m pyperf metadata telco.json
Metadata:
- aslr: Full randomization
- boot_time: 2016-10-19 01:10:08
- cpu_affinity: 2-3
- cpu_config: 2-3=driver:intel_pstate, intel_pstate:turbo, governor:performance, isolated; idle:intel_idle
- cpu_count: 4
- cpu_model_name: Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz
- description: Telco decimal benchmark
- hostname: selma
- loops: 8
- name: telco
- perf_version: 0.8.2
- performance_version: 0.3.3
- platform: Linux-4.7.4-200.fc24.x86_64-x86_64-with-fedora-24-Twenty_Four
- python_cflags: -Wno-unused-result -Wsign-compare -Wunreachable-code -DDYNAMIC_ANNOTATIONS_ENABLED=1 -DNDEBUG -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector-strong --param=ssp-buffer-size=4 -grecord-gcc-switches -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv
- python_executable: /home/haypo/prog/python/performance/venv/cpython3.5-68b776ee7e79/bin/python
- python_implementation: cpython
- python_version: 3.5.1 (64-bit)
- timer: clock_gettime(CLOCK_MONOTONIC), resolution: 1.00 ns

pyperf timeit¶

Usage¶

pyperf timeit usage:

python3 -m pyperf timeit
    [options]
    [--name BENCHMARK_NAME]
    [--python PYTHON]
    [--compare-to REF_PYTHON]
    [--inner-loops INNER_LOOPS]
    [--duplicate DUPLICATE]
    [-s SETUP]
    [--teardown TEARDOWN]
    [--profile PROFILE]
    stmt [stmt ...]

Options:

[options]: see Runner CLI for more options.
stmt: Python code executed in the benchmark. Multiple statements can be used.
-s SETUP, --setup SETUP: statement run before the tested statement. The option can be specified multiple times.
--teardown TEARDOWN: statement run after the tested statement. The option can be specified multiple times.
--name=BENCHMARK_NAME: Benchmark name (default: timeit).
--inner-loops=INNER_LOOPS: Number of inner loops per value. For example, the number of times that the code is copied manually multiple times to reduce the overhead of the outer loop.
--compare-to=REF_PYTHON: Run benchmark on the Python executable REF_PYTHON, run benchmark on Python executable PYTHON, and then compare REF_PYTHON result to PYTHON result.
--duplicate=DUPLICATE: Duplicate statements (stmt statements, not SETUP) to reduce the overhead of the outer loop and multiply inner loops by DUPLICATE (see --inner-loops option).
--profile=PROFILE: Run the benchmark inside the cProfile profiler and output to the given file. This is a convenient way to profile a specific benchmark, but it will make the actual benchmark timings much less accurate.

Note

timeit -n (number) and -r (repeat) options become -l (loops) and -n (runs) in pyperf timeit.

Example:

$ python3 -m pyperf timeit '" abc ".strip()' --duplicate=1024
.........................
Mean +- std dev: 104 ns +- 1 ns

Compare Python 3.8 to Python 3.6:

$ python3.8 -m pyperf timeit '" abc ".strip()' --duplicate=1024 --compare-to=python3.6
python3.6: ..................... 84.6 ns +- 4.4 ns
python3.8: ..................... 104 ns +- 0 ns

Mean +- std dev: [python3.6] 84.6 ns +- 4.4 ns -> [python3.8] 104 ns +- 0 ns: 1.23x slower (+23%)

Changed in version 1.6.0: Add --teardown option.

timeit versus pyperf timeit¶

The timeit module of the Python standard library has multiple issues:

It displays the minimum
It only runs the benchmark 3 times using a single process (1 run, 3 values)
It disables the garbage collector

pyperf timeit is more reliable and gives a result more representative of a real use case:

It displays the average and the standard deviation
It runs the benchmark in multiple processes
By default, it skips the first value in each process to warmup the benchmark
It does not disable the garbage collector

If a benchmark is run using a single process, we get the performance for one specific case, whereas many parameters are random:

Since Python 3, the hash function is now randomized and so the number of hash collision in dictionaries is different in each process
Linux uses address space layout randomization (ASLR) by default and so the performance of memory accesses is different in each process

See the Minimum versus average and standard deviation section.

pyperf command¶

New in version 1.1.

Measure the wall clock time to run a command, similar to Unix time command.

If the resource.getrusage() function is available, measure also the maximum RSS memory and stores it in command_max_rss metadata. In that case, --track-memory option can be used to use the RSS memory for benchmark values.

Usage¶

pyperf command usage:

python3 -m pyperf command
    [options]
    [--name NAME]
    [--track-memory]
    program [arg1 arg2 ...]

Options:

[options]: see Runner CLI for more options.
--track-memory: use the maximum RSS memory of the command instead of the time.
--name=BENCHMARK_NAME: Benchmark name (default: command).
program [arg1 arg2 ...]: the tested command.

Example measuring Python 3.6 startup time:

$ python3 -m pyperf command -- python3.6 -c pass
.....................
command: Mean +- std dev: 21.2 ms +- 3.2 ms

pyperf system¶

Get or set the system state for benchmarks:

python3 -m pyperf system
    [--affinity=CPU_LIST]
    [{show,tune,reset}]

Commands:

pyperf system show (or just pyperf system) shows the current state of the system
pyperf system tune tunes the system to run benchmarks
pyperf system reset resets the system to the default state

Options:

--affinity=CPU_LIST: Specify CPU affinity. By default, use isolate CPUs. See CPU pinning and CPU isolation.

See operations and checks of the pyperf system command and the Tune the system for benchmarks section.

pyperf collect_metadata¶

Collect metadata:

python3 -m pyperf collect_metadata
    [--affinity=CPU_LIST]
    [-o FILENAME/--output FILENAME]

Options:

--affinity=CPU_LIST: Specify CPU affinity. By default, use isolate CPUs. See CPU pinning and CPU isolation.
--output=FILENAME: Save metadata as JSON into FILENAME.

Example:

$ python3 -m pyperf collect_metadata
Metadata:
- aslr: Full randomization
- cpu_config: 0-3=driver:intel_pstate, intel_pstate:turbo, governor:powersave
- cpu_count: 4
- cpu_freq: 0=2181 MHz, 1=2270 MHz, 2=2191 MHz, 3=2198 MHz
- cpu_model_name:  Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz
- cpu_temp: coretemp:Physical id 0=51 C, coretemp:Core 0=50 C, coretemp:Core 1=51 C
- date: 2016-07-18T22:57:06
- hostname: selma
- load_avg_1min: 0.02
- perf_version: 0.8
- platform: Linux-4.6.3-300.fc24.x86_64-x86_64-with-fedora-24-Twenty_Four
- python_executable: /usr/bin/python3
- python_implementation: cpython
- python_version: 3.5.1 (64bit)
- timer: clock_gettime(CLOCK_MONOTONIC), resolution: 1.00 ns

pyperf slowest¶

Display the 5 benchmarks which took the most time to be run. This command should not be used to compare performances, but only to find “slow” benchmarks which makes running benchmarks taking too long.

Options:

-n: Number of slow benchmarks to display (default: 5)

pyperf convert¶

Convert or modify a benchmark suite:

python3 -m pyperf convert
    [--include-benchmark=NAME]
    [--exclude-benchmark=NAME]
    [--include-runs=RUNS]
    [--indent]
    [--remove-warmups]
    [--add=FILE]
    [--extract-metadata=NAME]
    [--remove-all-metadata]
    [--update-metadata=METADATA]
    input_filename.json
    (-o output_filename.json/--output=output_filename.json
    | --stdout)

Operations:

--include-benchmark=NAME only keeps the benchmark called NAME. The option can be specified multiple times.
--exclude-benchmark=NAME removes the benchmark called NAME. The option can be specified multiple times.
--include-runs=RUNS only keeps benchmark runs RUNS. RUNS is a list of runs separated by commas, it can include a range using format first-last which includes first and last values. Example: 1-3,7 (1, 2, 3, 7).
--remove-warmups: remove warmup values
--add=FILE: Add benchmark runs of benchmark FILE
--extract-metadata=NAME: Use metadata NAME as the new run values
--remove-all-metadata: Remove all benchmarks metadata except name and unit.
--update-metadata=METADATA: Update metadata: METADATA is a comma-separated list of KEY=VALUE

Options:

--indent: Indent JSON (rather using compact JSON)
--stdout writes the result encoded as JSON into stdout

Changed in version 1.2: The --include-benchmark and --exclude-benchmark operations can now be specified multiple times.

pyperf commands¶

pyperf show¶

pyperf compare_to¶

pyperf stats¶

pyperf check¶

pyperf dump¶

pyperf hist¶

pyperf metadata¶

pyperf timeit¶

Usage¶

timeit versus pyperf timeit¶

pyperf command¶

Usage¶

pyperf system¶

pyperf collect_metadata¶

pyperf slowest¶

pyperf convert¶

pyperf

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