Power up your work with compiling and profiling

Hello I am Cheuk

  • Open-Source contributor

  • Organisers of community events

  • ex EPS board member, PSF fellow
  • Looking for my next role


  • Presentation (30 mins)
  • Q&A and Break (15 mins)
  • Do Ex.1 and Ex.2 together (60 mins)
  • Q&A and Break (15 mins)
  • Do Ex. 3.1 and Ex. 3.2 on your own (90 mins)

Who has this problem - the code is too slow?

Profiling and Compling may have you

What is profiling?

Profiling (in software eng.)

  • Investigate software behaviour
  • When it is being executed
  • Dynamic analysis
  • the tool that does that is called a profiler

What is compiling?

Compiling (in software eng.)

  • convert a program into a machine code or lower-level form
  • in which the program can be executed
  • it takes time to compile codes
  • but lower-level codes are generally faster to execute

How can it help me?

It helps because

  • Analyse what makes my program slow
  • Avoid slow code
  • Or make slow code faster
  • Compiling makes repeated code faster

What is Numba

Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code.

What is JIT complier

  • just-in-time compiler
  • Python does not compile programs
  • It takes time to complie
  • If there are functions that got used more than once, the 2nd time running a complied version will be faster
  • Perfect for loops

What works with Numba

  • Numpy - what it is designed for
  • Loopings - things that is done more than once

Different modes

  • object mode
    - handles all values as Python objects
    - usually not faster
    - unless loop component can be "jit"

  • nopython mode
    - does not access the Python C API
    - high performamce

Different modes

  • @jit
    - automatically fall back to object mode
    - can compile code that failed in nopython mode
  • @njit
    - short hand for @jit(nopython=True)
    - garentee high performamce
    - if not supported it will failed to compile

Support CUDA GPUs

  • Numba supports CUDA GPU programming
  • Compiling a restricted subset of Python code
  • Allow CUDA kernels to have direct access to NumPy arrays
  • See PyData Amsterdam 2019 talk recording

Have the doc ready

and start learning

Using Numba Effectively Today

By Cheuk Ting Ho

Using Numba Effectively Today

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