The State of PyPy (April 2015)

Modern programming languages are expected to offer high-level abstractions at reasonable speed. Especially scripting languages like Ruby, PHP and Python are highly dynamic and flexible, which makes performance optimizations difficult to implement.

While Python 3 offers a lot more features compared to Python 2, these features don't always improve runtime performance. In the pystone benchmark, execution speed has even decreased over the recent years.
PyPy, a relatively new implementation of Python with a focus on performance tries to improve the situation - with obvious success. Code execution is much faster thanks to the built-in just in time compiler which optimizes code at runtime.

In this talk I give an overview of PyPy and how it works internally. Also I talk a bit about PyPy STM, a new approach for parallel code execution that can take advantage of modern multi-core hardware.

Transcript

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 loop 10 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: