A naukri.com initiative
Towards Data Science
1w
314
How to Optimize your Python Program for Slowness
- The article explores how to make Python programs run absurdly slower by examining different rule sets and building small Python programs to run for an extraordinarily long time.
- Starting with the most permissive rule set of allowing infinite loops, the article demonstrates a simple program using 'while True' that runs forever.
- Moving on to rule set 2, the article introduces nested fixed-range loops to run for an impractically long time with a small amount of memory usage.
- Rule set 3 introduces a 5-state Turing machine with infinite, zero-initialized memory, running for 47,176,870 steps before halting.
- The article progresses to a 6-state Turing machine known to run for over 10^↑↑15 steps, showing a video of its first 10 trillion steps.
- Rule set 5 delves into building a Python program to compute 10^↑↑15 steps using increment, addition, multiplication, exponentiation, and tetration functions.
- The article emphasizes the impact of Python's immutable int type on performance, advocating the use of gmpy2.xmpz for in-place updates.
- By constructing these programs, the article highlights the capabilities of nested loops, Turing machines, and hand-inlined functions to demonstrate incredibly slow program runtimes.
- The exploration of tetration unveils a hierarchy of hyperoperations beyond exponentiation, showcasing the potential to express unimaginably large numbers compactly.
- The article concludes by providing insights on writing programs that run for an extensive duration, showcasing the importance of efficient coding practices and minimal systems.
- The author invites readers to explore the open-source GitHub repository for all the code discussed in the article.
Read Full Article
17 Likes
For uninterrupted reading, download the app