Power up your work with compiling and profiling

GitHub repo:

https://github.com/Cheukting/using-numba

Hello I am Cheuk

• Open-Source contributor
  
  
   
• Organisers of community events
  
  
   
• ex EPS board member, PSF fellow
   
• Looking for my next role

Outline

• 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

https://numba.readthedocs.io/en/stable/index.html

and start learning

https://github.com/Cheukting/using-numba