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![$ python
Python 2.7.13 (default, Dec 18 2016,
07:03:39)
>>> print ("Hello World")
Hello World
>>>
>>> [x+1 for x in xrange(0,20, 2)]
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
>>>
$ python your-application.py
I'm a python application
$ pypy
Python 2.7.13[PyPy 5.8.0 with GCC 4.2.1]
on darwin
>>>> print ("Hello World")
Hello World
>>>>
>>> [x+1 for x in xrange(0,20, 2)]
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
>>>
$ pypy your-application.py
I'm a python application
Compliant
1](https://image.slidesharecdn.com/pypy-170912101539/85/Using-PyPy-instead-of-Python-for-speed-6-320.jpg)
![$ python
Python 2.7.13 (default, Dec 18 2016,
07:03:39)
>>> print ("Hello World")
Hello World
>>>
>>> [x+1 for x in xrange(0,20, 2)]
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
>>>
$ python your-application.py
I'm a python application
$ pypy
Python 2.7.13[PyPy 5.8.0 with GCC 4.2.1]
on darwin
>>>> print ("Hello World")
Hello World
>>>>
>>> [x+1 for x in xrange(0,20, 2)]
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
>>>
$ pypy your-application.py
I'm a python application
Compliant
1](https://image.slidesharecdn.com/pypy-170912101539/75/Using-PyPy-instead-of-Python-for-speed-6-2048.jpg)
Uploaded byEnplore AB
Using PyPy instead of Python for speed
The document discusses the benefits of using PyPy, a JIT-compiled Python implementation, for improving code execution speed. It highlights real-world performance comparisons showing significant speed increases in processing tasks, particularly with heavy CPU loads. The author, Niklas Bivald, shares their experience using PyPy in production since 2014, managing large datasets and multiple applications.
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Similar to Using PyPy instead of Python for speed
Using PyPy instead of Python for speed
- 1. “If you wantyour code to run faster, you should probably just use PyPy.” — Guido van Rossum @nbivald niklas.bivald@enplore.com
- 2. Python implementation witha JIT compiler stable 2 compliant 1 fastPyPy is a and 3
- 3. Since 2003 PyPyhas received funding from the Python Software Foundation, EU, Google and Mozilla
- 4.
- 5. - Visualisation - (Lotsof) Statistics - From classifications to regressions Data Parsers Analytics ... in it's rawest form ... bespoke, in Python
- 6. $ python Python 2.7.13(default, Dec 18 2016, 07:03:39) >>> print ("Hello World") Hello World >>> >>> [x+1 for x in xrange(0,20, 2)] [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] >>> $ python your-application.py I'm a python application $ pypy Python 2.7.13[PyPy 5.8.0 with GCC 4.2.1] on darwin >>>> print ("Hello World") Hello World >>>> >>> [x+1 for x in xrange(0,20, 2)] [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] >>> $ pypy your-application.py I'm a python application Compliant 1
- 7. We've used PyPyin production since 2014, processing: ◦ Millions of flights ◦ Petabytes of data ◦ Thousands upon thousands of servers spun up ◦ 10 000 LOC + applications Stable 2
- 8. $ time python-c " > l = 0 > for i in xrange(0,10000): > for ii in xrange(0, 10000): > l = i+ii > print(l) > " 19998 real 0m8.101s user 0m7.999s time pypy -c " > l = 0 > for i in xrange(0,10000): > for ii in xrange(0, 10000): > l = i+ii > print(l) > " 19998 real 0m0.201s user 0m0.174s Fast 3 SYNTHETIC BENCHMARK
- 9. Each second offlight data is divided into 256 words á 12 bit. Each 12 bit section contains between 3 to 12 parameters. To extract, we iterate bit-by-bit on the continuous byte stream.
- 10. $ pypy run-file.py AircraftDfdrDataPoint:created a new dfdr datapoint Task parse.blackbox.data succeeded in 18.82s $ python run-file.py AircraftDfdrDataPoint: created a new dfdr datapoint Task parse.blackbox.data succeeded in 46.25s Fast 3 REAL WORLD BENCHMARK
- 11.
- 12. the Python weknow CPU Heavy tasks ... preferbly > 1s... and love
- 13.
- 14. No C NoNumpy ... unless cffi ... coming soon No Py3 ... coming soon
- 15.
- 16. Web API:s? ... suchas Flask
- 17.