python-gc-benchmark

This is a Python Benchmark suite that focuses on Memory Usage and Heap Analysis. The micro-benchmarks have been chosen for memory intensive programs Intended for anyone that wants to analyse memory Usage of different Python versions.

The suite supports Python 2, Python 3, PyPy 2 and PyPy 3.

Implementation Criteria

The benchmarks feature programs with large demands for memory and are mostly array intensive programs like scientific computing problems, some games and other general programs that are container intensive.

The memory Information analysed by the suite include:

• RSS
• VMS
• Shared Memory
• USS and
• PSS
• Heap Analysis

The main metric considered is the Resident Set Size(RSS) which is the allocated memory for a program. This is chosen because;

• It is machine-independent. Though programs will use more memory on 64-bit machines than 32-bit machines.
• Stretches the memory manager as it puts a lot of pressure on it.

Limitations

The metrics on Resident Set Size seem to over report for multi-threaded programs. In the event that two threads share memory, the reported RSS comprises individual memory usage of this memory for each thread.

Therefore this metric is unsuitable for Multiprocessing and multi-threaded programs. Consider other metrics other that RSS in these scenarios.

The suite should be launched on Python 3 but benchmarks will be run with the specified Python version available as an option.

Run the benchmarks from source

Get the source code:

git clone http://gitlab.casa.cs.unb.ca/jnanjeky/python_gc_benchmark
cd python-gc-benchmark

## Install dependencies for both Python 2 and 3
pip install -r requirements.txt

# Benchmark suite uses Python3
python3 setup.py install

Run a specific benchmark:

python-gc-benchmark run -p <python version> -b <benchmark>

View the heap:

python-gc-benchmark heap -p <python version> -b <benchmark>

View objects on the heap not reachable from the roots after GC :

python-gc-benchmark heapu -p <python version> -b <benchmark>

Run all benchmarks:

python-gc-benchmark run -p <python version>

Download from PyPI

Use pip to install the package:

pip install python-gc-benchmark

Run a specific benchmark:

python-gc-benchmark run -p <python version> -b <benchmark>

Run all benchmarks:

python-gc-benchmark run -p <python version>

Examples

Let us run some sample benchmarks :

Python 2 :

gitpod /workspace/python-gc-benchmark $ python-gc-benchmark run -p python2 -b python_gc_benchmark/benchmarks/2to3.py
Memory Benchmark for : python_gc_benchmark/benchmarks/2to3.py
        RSS:     786432 kb
        VMS:    5582848 kb
        Shared:     720896 kb
        Data:     323584 kb
        USS:     126976 kb
        PSS:     192512 kb

Python 3 :

gitpod /workspace/python-gc-benchmark $ python-gc-benchmark run -p python3 -b python_gc_benchmark/benchmarks/2to3.py
Memory Benchmark for : python_gc_benchmark/benchmarks/2to3.py
        RSS:     794624 kb
        VMS:    5582848 kb
        Shared:     729088 kb
        Data:     323584 kb
        USS:     126976 kb
        PSS:     192512 kb

And now, Heap Analysis :

gitpod /workspace/python-gc-benchmark $ python-gc-benchmark heap -p python3 -b python_gc_benchmark/benchmarks/2to3.py
Partition of a set of 16 objects. Total size = 1952 bytes.
Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
    0      1   6      496  25       496  25 types.FrameType
    1      1   6      240  12       736  38 dict of _io.FileIO
    2      1   6      240  12       976  50 dict of psutil.Popen
    3      1   6      240  12      1216  62 dict of subprocess.Popen
    4      1   6      176   9      1392  71 _io.BufferedReader
    5      2  12      128   7      1520  78 tuple
    6      1   6       80   4      1600  82 psutil._pslinux.Process
    7      1   6       72   4      1672  86 _io.FileIO
    8      1   6       56   3      1728  89 psutil.Popen
    9      1   6       56   3      1784  91 subprocess.Popen
<4 more rows. Type e.g. '_.more' to view.>

Finally, Heap Analysis After Garbage Collection :

gitpod /workspace/python-gc-benchmark $ python-gc-benchmark heapu -p python3 -b python_gc_benchmark/benchmarks/2to3.py
Data from unreachable objects.
Partition of a set of 741 objects. Total size = 171990 bytes.
Index  Count   %     Size   % Cumulative  % Type
    0     83  11    77848  45     77848  45 dict
    1     46   6    39744  23    117592  68 type
    2     50   7    11200   7    128792  75 set
    3    124  17     9920   6    138712  81 types.WrapperDescriptorType
    4    116  16     9280   5    147992  86 builtins.weakref
    5     94  13     6768   4    154760  90 types.BuiltinMethodType
    6     92  12     5608   3    160368  93 tuple
    7     55   7     3960   2    164328  96 types.MethodDescriptorType
    8     53   7     3816   2    168144  98 types.MemberDescriptorType
    9     14   2     2166   1    170310  99 str
<6 more rows. Type e.g. '_.more' to view.>

Note, examples are running on this specification :

gitpod /workspace/python-gc-benchmark $ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description:    Ubuntu 19.04
Release:        19.04
Codename:       disco
gitpod /workspace/python-gc-benchmark $ python2
Python 2.7.15 (default, Aug 20 2019, 10:48:33)
[GCC 8.3.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
gitpod /workspace/python-gc-benchmark $ python3
Python 3.7.2 (default, Aug 20 2019, 10:51:20)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
gitpod /workspace/python-gc-benchmark $

Future work

• Add other useful commands.
• Investigate Ubuntu s390x .

References

https://github.com/nuprl/retic_performance

https://github.com/python/pyperformance

http://www.marinamele.com/7-tips-to-time-python-scripts-and-control-memory-and-cpu-usage