https://github.com/mikma03/high-performance-python

High-performance Python in practice. Examples of code optimization using various methods and packages.
https://github.com/mikma03/high-performance-python

advanced-programming code-optimization code-profiling cython numba oop python3

Last synced: about 1 month ago
JSON representation

High-performance Python in practice. Examples of code optimization using various methods and packages.

• Host: GitHub
• URL: https://github.com/mikma03/high-performance-python
• Owner: Mikma03
• License: mit
• Created: 2021-12-15T22:03:43.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-05-24T05:32:52.000Z (over 3 years ago)
• Last Synced: 2025-02-26T10:45:01.411Z (10 months ago)
• Topics: advanced-programming, code-optimization, code-profiling, cython, numba, oop, python3
• Language: Python
• Homepage: https://github.com/Mikma03/High-performance-Python
• Size: 13.8 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# High-performance-Python

- [High-performance-Python](#high-performance-python)

- [Detailed information in chapters](#detailed-information-in-chapters)

- [Additionals materials](#additionals-materials)

# Detailed information in chapters

* Chapter 1: Profiling

    - What are the ways to profile your code?

    

    - What insights can I gain from a profile?

* Chapter 2: Understanding Performant Programming

    - How does a computer work and how does that affect my code performance?

* Chapter 3: Lists and Tuples

    - How do lists and tuples work?

    

    - What are the performance implications of this?

* Chapter 4: Dictionaries and Sets

    - How do dictionaries and sets work?

    

    - What are the performance implications of this?

    

* Chapter 5: Iterators

    - How do iterators work?

    

    - How can I use them to reduce memory in my code?

    

    - How can I use them to reduce the CPU operations needed in my code?

* Chapter 6: Matrix and Vector Computation

  - How does the CPU perform matrix/vector operations?

  

  - What are the ways I can profile the efficiency of these operations?

  

  - How can I speed up code given a profile?

* Chapter 7: Compiling to C

    - What are the automated ways to compile my python to C?

    

    - What are the manual ways to compile my python to C?

    

    - How can I use these technologies to speed up my code and not slow down development?

    

* Chapter 8: Concurrency

    - How does concurrency work and how does python support it?

    

    - How can I speed up multiple IO operations?

    

    - How can I hide IO wait?

* Chapter 9: Multiprocessing

    - What is multiprocessing?

    

    - How can I speed up my code on a multiple CPU machine?

    

    - What are the subtleties of muli-CPU code?

* Chapter 10: Clusters and Job Queues

    - How can I extend my code to a cluster?

    

    - What are some of the pain-points with clusters?

    

    - What is a queue and how is it useful?

* Chapter 11: Using Less Ram

    - How can I use algorithms to reduce the RAM usage of my code?

 

    - What are tries and probabilistic data structures?

# Additionals materials

- High-Performance Computing with Python 3.x

    - https://subscription.packtpub.com/video/application-development/9781789956252/101415/101437/introduction-to-cython