PyPy is a fast runtime for Python. It can speed up execution time up to 7x compared to normal CPython for sequential tasks. Using a Just-In-Time compiler it has a similar approach as HHVM for PHP and Rubinius for Ruby. This talks gives an overview of the current project goals, namely improving parallel execution speed with PyPy STM and improving C-Extension compatibility.

1. PyPy
The state of
2015-04 @matthiasendler

2. Why PyPy?
"Scientific American" Vol. XXXVI, No. 19, May 12, 1877 p.294 New York: Munn &Company (Publisher)

3. Why PyPy?
What keeps you from using switching to Python 3?

4. Why PyPy?
No more support for older versions
Major bugfixes
New language features
Improved performance
Incentives to switch to a new language version

5. Why PyPy?
More speed means…
less servers
new possibilities

6. Why PyPy?
Two ways to speed up a scripting language
•Improve the implementation
•Improve the runtime

7. Why PyPy?
Two ways to speed up a scripting language
•Improve the implementation
•Improve the runtime

8. Why PyPy?
Warning:
Benchmarks are always error-prone.
Always measure yourself!
This is just a trend.

9. 0
35
70
105
140
1.8.7 1.9.3 2.0.0 2.1.0 2.2.0
Relative Ruby performance
Why PyPy?
Source: isrubyfastyet.com
speed (%)
version
baseline (100%)
+12%
+30%
+35% +36%

10. Why PyPy?
0
65
130
195
260
5.2 5.3 5.4 5.5 5.6 7.0
Relative PHP performance
Source: PHPNG a new core for PHP7 by Dmitry Stogov
baseline (100%)
speed (%)
version
+13%
+36% +39%
+50%
+149%

11. Why PyPy?
25
50
75
100
2.6 2.7 3.4
Relative Python performance
Source: pystone benchmark on Core i7, 16 GB RAM
baseline (100%)
speed (%)
version
-10%
-3%

12. Why PyPy?
0
38
75
113
150
2007 2009 2011 2013 2015
PHP Python Ruby
baseline (100%)
speed (%)
time
Relative performance improvements per language over time

13. Why PyPy?
The net result of the 3.0 generalizations is that
Python 3.0 runs the pystone benchmark around 10% slower than Python 2.5.
Most likely the biggest cause is the removal of special-casing for small
integers. There’s room for improvement, but it will happen after 3.0 is
released!
docs.python.org/3/whatsnew/3.0.html

14. Why PyPy?
Two ways to speed up a scripting language
•Improve the implementation
•Improve the runtime

15. Why PyPy?
Ruby Rubinius
PyPy
HHVM
Python
PHP

16. About PyPy
Cottrell & Babcock's air spring printing press
Source: Scientific American, June 22, 1872

17. What is PyPy?
Followup of Psyco runtime
Version 1.0 in 2007
Python written in Python
Just-In-Time compiler
Compatible with Python 2.7 and 3.4

18. What is PyPy?
Followup of Psyco runtime
Version 1.0 in 2007
Python written in Python RPython
Just-In-Time compiler
Compatible with Python 2.7 and 3.4

19. PyPy implementation
• Variables in any given control flow path must have only a single type
• No multiple inheritance
• No generators
• Most dunder-methods can't be used.
RPython is a subset of Python
http://www.quora.com/PyPy/How-annoying-is-writing-RPython-compared-to-Python

20. PyPy implementation
Tracing JIT compiler
http://www.aosabook.org/en/pypy.html
Your Python program

21. PyPy implementation
Tracing JIT compiler
http://www.aosabook.org/en/pypy.html
„Hot“ loop

22. PyPy implementation
Tracing JIT compiler
http://www.aosabook.org/en/pypy.html
can_enter_jit()
jit_merge_point()

23. PyPy implementation
Tracing JIT compiler
http://www.aosabook.org/en/pypy.html
Optimized machine code

24. PyPy Speed
speed.pypy.org

25. Upright boring machine, 1872
Source: Scientific American, Nov 23,1872
Demo

26. The future of PyPy
Berryman's steam trap, 1872
Source: Scientific American, Sept 14,1872

27. Project milestones
2008 Django support
2010 First JIT-compiler
2011 Compatibility with CPython 2.7
2014 Basic ARM support
CPython 3 support
Improve compatibility with C extensions
NumPyPy
Multi-threading support

28. Project milestones
2008 Django support
2010 First JIT-compiler
2011 Compatibility with CPython 2.7
2014 Basic ARM support
CPython 3 support
Improve compatibility with C extensions
NumPyPy
Multi-threading support

29. PyPy STM

30. PyPy STM
http://dabeaz.com/GIL/gilvis/
GIL locking

31. PyPy STM
10 loops, best of 3: 1.2 sec per loop10 loops, best of 3: 822 msec per loop
from threading import Thread
def count(n):
while n > 0:
n -= 1
def run():
t1 = Thread(target=count, args=(10000000,))
t1.start()
t2 = Thread(target=count, args=(10000000,))
t2.start()
t1.join(); t2.join()
def count(n):
while n > 0:
n -= 1
def run():
count(10000000)
count(10000000)
Inside the Python GIL - David Beazley

32. PyPy STM
T1
T2
T3
x
y
z
Thread 1
Shared memory
T1
T2
T3
Thread 2

33. PyPy STM
T1
T2
T3
x = 3
x
y
z
Thread 1
Shared memory
T1
T2
T3
y=5
Thread 2

34. PyPy STM
T1
T2
T3
3
5
x
y
z
Thread 1
Shared memory
T1
T2
T3
Thread 2

35. PyPy STM
T1
T2
T3
x = 18
y= 12
3
5
x
y
z
Thread 1
Shared memory
T1
T2
T3
x = 2
Thread 2

36. PyPy STM
T1
T2
T3
2
5
x
y
z
Thread 1
Shared memory
T1
T2
T3
Thread 2

37. Lathrop's sewing machine, 1872
Source: Scientific American, Oct 26,1872
Demo

38. Conclusion
…already awesome for sequential tasks
…gradually improving parallel performance
…a very important Python project
PyPy is…

39. Further reading
Python 3 Performance
https://speakerdeck.com/pyconslides/python-3-dot-3-trust-me-its-
better-than-python-2-dot-7-by-dr-brett-cannon
https://www.youtube.com/watch?v=UJPdhx5zTaw

40. Further reading
RPython
http://tratt.net/laurie/blog/entries/
fast_enough_vms_in_fast_enough_time

41. Further reading
PyPy STM
http://pypy.readthedocs.org/en/latest/stm.html
http://morepypy.blogspot.de/
Demo code from
http://www.ripton.net/blog/?p=104

42. Blueprints provided by Marcel Douwe Dekker under Creative Commons.
https://www.flickr.com/photos/marceldouwedekker/7564782310/in/
photostream/
Blueprint by Joy Oil Co Ltd (http://commons.wikimedia.org/wiki/
File:Joy_Oil_gas_station_blueprints.jpg)
Credits

43. by Simon Child
and by Luis Prado
by Océan Bussard
by Luis Rodrigues
by Mister Pixel
by Alexander Wiefel
by Darin S
by spotted paint
The icons used in this presentation were downloaded from The Noun Project.
Thanks to the following artists: