PurpleMelbourne
Chieftain
- Joined
- Jul 15, 2015
- Messages
- 7
Hello fellow Civ4 people!
I started playing Civilization on Amiga 500, and haven't stopped... And as you know, that means I've spent way too much time waiting for the computer players to finish their turns! ;-)
So I've wondered about a lot of ideas for optimisations which have been beyond my abilities. But now with the help of AI, it's a lot more possible to learn the required steps to get through these things.
I've been exploring ways to optimize the Python code in Civilization 4, particularly to improve late-game performance when AI turns can take a long time. With the assistance of AI tools, I've come up with some ideas that I wanted to share and discuss with the community:
Looking forward to your input and insights!
I started playing Civilization on Amiga 500, and haven't stopped... And as you know, that means I've spent way too much time waiting for the computer players to finish their turns! ;-)
So I've wondered about a lot of ideas for optimisations which have been beyond my abilities. But now with the help of AI, it's a lot more possible to learn the required steps to get through these things.
I've been exploring ways to optimize the Python code in Civilization 4, particularly to improve late-game performance when AI turns can take a long time. With the assistance of AI tools, I've come up with some ideas that I wanted to share and discuss with the community:
- Profiling and identifying bottlenecks: Using Python profiling tools like cProfile or Intel VTune Amplifier to analyze the game's Python scripts and identify performance hotspots. This can help pinpoint specific functions or loops that are taking the most time.
- Optimizing loops and data structures: Reviewing the code for opportunities to optimize loops, such as using list comprehensions instead of traditional loops, or replacing slow data structures with more efficient ones like dictionaries or sets.
- JIT compilation with Numba: Experimenting with using Numba, a just-in-time (JIT) compiler for Python, to accelerate math-heavy portions of the code. Numba can compile Python code to native machine instructions, taking advantage of Intel's SIMD instructions for parallel processing.
- Cython for performance-critical sections: Rewriting computationally intensive parts of the Python code in Cython, which compiles to C and can provide significant speedups. This could be especially useful for sections that interface with the game's C++ codebase.
- Multithreading with joblib or multiprocessing: Investigating the use of Python's multithreading or multiprocessing libraries to parallelize certain tasks, like AI decision-making or game state updates. This could help leverage the multiple cores in modern Intel and AMD CPUs.
- Intel Distribution for Python: Trying out the Intel Distribution for Python, which includes optimizations for Intel CPUs and can potentially boost performance out of the box. It includes optimized versions of NumPy, SciPy, and other key libraries.
- AMD AOMP and ROCm for AMD CPUs: For those with AMD CPUs, exploring the use of AMD's AOMP (AMD Optimizing C/C++ Compiler) and ROCm (Radeon Open Compute) platform, which provide optimizations and libraries tailored for AMD hardware.
Looking forward to your input and insights!