examples: Adding Sugarscape IG - polars with numba vectorized loop

[adamamer20]

This PR introduces a Numba implementation for the loop in SugarscapePolars to optimize agent move calculations. Key points:

1. Implementation: Added a Numba-accelerated function to determine the best moves for agents, replacing the more complex DataFrame-based logic.

2. Validation: Implemented equality checks across different Polars implementations to ensure consistent results.

3. Performance Comparison:

   • Numba-CPU and Numba-Parallel are the fastest.
   • DataFrame looping is now the slowest due to added complexity for correct agent priority handling.
   • Numba GPU implementation is slower than non-vectorized CPU, likely due to data transfer overhead and the nature of the parallelism in this problem.

4. Numba Considerations:

   • Requires explicit type and size specifications for vectors.
   • Input vector must match output size.
   • Limited to 1D vector operations.

5. Performance Bottlenecks:

   • Major time consumption in join operations, particularly in neighborhood calculations and cell updates.
   • Potential for optimization with lazy evaluation and an improved backend.

6. Future Optimizations:

   • Explore lazy evaluation approaches for join operations.
   • Non-vectorized loop directly on Polars DataFrame (without converting to numpy vectors) might offer potential speed improvements.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

[adamamer20]

This is ready.

[rht]

Why is the previous one, although more concise, not considered favorable?

[adamamer20]

Do you mean the previous implementation?

[rht]

Yes.

[adamamer20]

The pl.col("blocking_agent_order").is_null() is to allow agents to move if there is no blocking agent.

I changed the title for clarity

[rht]

Approved because my comments are questions instead of blocking requests.