PyCon JP 2015. Introduce dis module and various profiler

1. Writing Fast Code
PyCon JP 2015
iam@younggun.kim

2. Younggun, Kim
http://younggun.kim
@scari_net
scari

3. Badass Alien @ District 9, SMARTSTUDY
http://pinkfong.com
PyCon Korea Organizer
http://pycon.kr
We’ll host PyCon APAC 2016 at Seoul

4. What I Think My Code Run
Movie - The Good The Bad The Weird, 2008

5. How My Code Really Run
The Killers : All These Things That I’ve Done M/V
https://youtu.be/sZTpLvsYYHw

6. Objective

7. 1. Understanding
How Computer Works

8. 2. How to use
Profiler

9. But why?

10. Say, thousands of people using your
code everyday and if you save 1
second to run it, this means you
could save over 4 days of time
human race wasted per a year.

11. See How Computer Works
and How Fast Computer
and it s peripherals

12. I/O >> 4D Wall >> Memory

13. Morse Code
Modem (2400)
CDMA(2G)
HSPA(3G, DL)
LTE*
USB 2.0
802.11n
USB 3.0
SATA 3.0
Thunderbolt 2
DDR2 1066Mhz
DDR3 1600Mhz
≈ 21 bps
≈ 2400 bps
≈ 153 kbit/s
≈ 13.98 Mbit/s
≈ 100 Mbit/s
≈ 480 Mbit/s
≈ 600 Mbit/s
≈ 3 Gbit/s
≈ 6 Gbit/s
≈ 20 Gbit/s
≈ 64 Gbit/s
≈ 102.4 Gbit/s
https://en.wikipedia.org/wiki/List_of_device_bit_rates

14. Yes! Memory is
blazing fast! (Really?)

15. DDR3 1600Mhz
FSB 400 (old Xeon)
PCI Express 3.0 (x16)
QuickPath Interconnect
HyperTransport 3.1
L3 Cache(i7-4790X)
L2 Cache(i7-4790X)
≈ 12.8 GB/s
≈ 12.8 GB/s
≈ 16 GB/s
≈ 38.4 GB/s
≈ 51.2 GB/s
≈ 170 GB/s
≈ 308 GB/s
Nope!

16. Computer Knows Only 0 and 1

17. 00100000001000100000000101011110
Like This

18. 00100000001000100000000101011110
opcode
addr 1
addr 2
value
MIPS32 Add Immediate instruction (ADDI)
addi $r1, $r2, 350
$r1 = $r2 + 350

19. Computer Execute These Instruction
per clock basis

20. Clock (Hz)

24. 1Hz

25. 1Hz
L1 Cache Acces
L2 Cache Access
L3 Cache Access
RAM Access
SSD I/O
HDD I/O
Internet: Tokyo to SF
Run IPython (0.6s)
Reboot (5m)
3s
9s
43s
6m
2-6 days
1-12 months
12 years
63 years
32,000 years!!

26. Hey! This is PyCon!

27. How Do You Know Python Works?
Neon Genesis Evangelion

28. そのための dis です
Neon Genesis Evangelion

29. Disassemble Python Code To
CPython Bytecode
To Support Analysis
dis module
https://github.com/python/cpython/blob/master/Include/opcode.h

31. line # of source
op addr / instruction annotations
param

32. An Empty List Creation
[] vs list()

36. Dictionary
{} vs dict()

40. Find an element in a list
using for-loop vs in

45. A tool for dynamic program analysis
that measure the space or time
complexity of a program.
Profilers

46. • cProfile (profile)
• hotshot
• line_profiler
• memory_profiler
• yappi
• profiling
• pyinstrument
• plop
• pprofile

47. cProfile
• built-in profiling tool
• hook into the VM in CPython
• introduces overhead a bit
https://docs.python.org/3.5/library/profile.html

48. cProfile
python -m cProfile python_code.py

49. line_profiler
• can profile line-by-line basis
• Uses a decorator to mark the
chosen function (@profile)
• introduces greater overhead
https://github.com/rkern/line_profiler

50. profiling
• Interactive Python profiler which
inspired from Unity3D Profiler
• Keep the call stack.
• Live Profiling
• Only Support Linux
https://github.com/what-studio/profiling

51. https://github.com/sublee/pyconkr2015-profiling-resources/blob/master/continuous.gif

52. fibona
Use profiler with real code

53. fibona
Korean Fried Chicken Served as one
chicken. (not pieces)
And it’s quite complex to determine
how many chicken would enough for
N people.

54. fibona
The problem can be solved easily
using fibonacci number.
1 1 2 3 5 8 13 21 34 …
For Nth fibonacci number of people,
N-1 th fibonacci number of chicken
would be perfect.

55. fibona
Awesome Idea! but how do you get
enough chicken if number of the
people is not an fibonacci number?

56. fibona
Apply Zeckendorf’s theorem,
which is about the representation of
integers as sum of Fibonacci number

57. https://en.wikipedia.org/wiki/Zeckendorf's_theorem

63. 目標をセンターに入れて
プロファイリング

64. cProfile
python -m cProfile fibonachicken.py

65. cProfile

66. line_profiler

67. line_profiler
kernprof -l -v fibonachicken.py

68. line_profiler

69. line_profiler

70. line_profiler

71. line_profiler

72. Both fib() and is_fibonacci() is the bottleneck.
Should replace these with better one

73. Hypothesis #1
Improvement of fib() could result
better performance

74. Binet s Formula
https://en.wikipedia.org/wiki/Jacques_Philippe_Marie_Binet

75. cProfile

76. Hypothesis #2
Can we improve is_fibonacci() not to
use fib() at all?

77. n is a Fibonacci number if and only if 5n*n+4 or
5n*n-4 is a square
Gessel s Formula
http://www.maths.surrey.ac.uk/hosted-sites/R.Knott/Fibonacci/fibFormula.html

78. cProfile

79. Thanks!