https://github.com/bbengfort/fastlife
Experimenting with C code and Python bindings
https://github.com/bbengfort/fastlife
cellular-automata cython game-of-life parallel-computing
Last synced: 2 months ago
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Experimenting with C code and Python bindings
- Host: GitHub
- URL: https://github.com/bbengfort/fastlife
- Owner: bbengfort
- License: bsd-3-clause
- Created: 2020-07-26T12:30:21.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-03T20:00:41.000Z (over 1 year ago)
- Last Synced: 2025-07-26T06:30:40.881Z (3 months ago)
- Topics: cellular-automata, cython, game-of-life, parallel-computing
- Language: Python
- Homepage: https://fastlife.readthedocs.io/
- Size: 51.8 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fast Life
[](https://travis-ci.com/bbengfort/fastlife)
[](https://codecov.io/gh/bbengfort/fastlife)
[](https://fastlife.readthedocs.io/en/latest/?badge=latest)
Fast Life is an experiment in Python simulation performance. Python has many excellent simulation frameworks including [SimPy](https://simpy.readthedocs.io/en/latest/) and [MESA](https://mesa.readthedocs.io/en/master/index.html), which make it easy to conduct simulations for research and experimental purposes. Fast Life is not intended to be a simulation framework in the same way, instead Fast Life is designed to answer the question: "Can Python be used to create extremely large scale simulations"? To this end, Fast Life implements a seemingly simple simulation: [Conway's Game of Life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) -- but scales it up to massive proportions. We then explore several different implementations including:
1. Pure Python Sequential
2. Pure Python Multiprocessing
3. C-bindings Sequential
4. C-bindings Parallel
Each of these implementations will be benchmarked with significantly sized worlds and the same random seeds to determine how their performance scales. Please see [fastlife.readthedocs.io](https://fastlife.readthedocs.io/en/latest/) for more details and the complete write up on the documentation.
## Quick Start
Install the package and the command line tool using the Python package manager as follows:
```
$ pip install fastlife
```
Alternatively, if you're interested in development, you can download the repository, cd into it and install locally with:
```
$ git clone https://github.com/bbengfort/fastlife.git
$ cd fastlife
$ pip install -e .
```
Note that if you're developing, you should probably set up a virtualenv and all of that good stuff before doing this step. Once done, you should have a CLI script `fastlife` installed on your path, check it out as follows:
```
$ fastlife --help
```
To run a simulation and see the animated action, you would use `fastlife run -a` - you can play with the various settings and commands to see different implementations of the simulation.