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Optimized python 25 passes -> 4,848 passes #15

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Optimized python 25 passes -> 4,848 passes #15

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michhernand
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Changes include:

  1. Adding numpy library and using arrays instead of lists. This optimization results in about a 100% speed improvement.
  2. The real improvement comes from numba, which builds off of numpy and jit compiles our primary worker function. This optimization results in about a 19,000% speed improvement.
  3. Also refactored to a more function-based approach.
  4. Added ability to pass arguments (with default values) via command line.

Michael Hernandez added 2 commits March 26, 2021 01:45
Initial commit of requirements.txt.
4c65f72 
Added numpy and numba for python optimization.
Initial commit of numba-optimized python code
db99432 
Benchmarks on Core-i5 Laptop Running Windows (Using a limit of 1,000,000):
*Measured in number of passes in 5 seconds*

Original Version:              25
Numpy Optimized (without JIT): 57   (128% improvement)
Numba Optimized (with JIT):    4848 (19,292% improvement)

Other changes include:
- Refactoring to use functions (for debugging numba).
- Added ability to pass args via command line.
@michhernand
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Closing as #9 seems equally or more optimal - and closer to the original source

@davepl
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davepl commented Mar 26, 2021 via email

@michhernand
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Makes sense! Would love to see a sequel to this comparison with additional packages / frameworks included.

Btw - great videos! Keep them coming.

callowaysutton added a commit to callowaysutton/Primes that referenced this pull request Mar 29, 2021
@callowaysutton
Huge Improvement on Python
0069cd5 
435,000% improvement on PlummersSoftwareLLC#15 's 19,000% improvement on the original version by using the Python 3 built-in LRU Caching decorator. Can be used in Python 2.7 by installing the functools library as well.

This should be competitive with even the C++ version :D
@callowaysutton callowaysutton mentioned this pull request Mar 29, 2021
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@michhernand @davepl