perf(core): optimize primary evaluation loops and state updates

[Diwakar-odds]

Summary

The primary execution loops and state updates are currently unoptimized, leading to potential performance bottlenecks under heavy load. By refactoring these loops and implementing batch processing or memoization, we can significantly reduce execution time.

Motivation

Performance is critical for user experience and system scalability. Optimizing the hottest paths in the code ensures that the application remains responsive as the dataset or user base grows.

Acceptance Criteria

• Profile the core execution loop to identify bottlenecks.
• Implement batch processing or memoization to reduce redundant operations.
• Add performance benchmarks to quantify the improvement.

How to Test

• Run the new performance benchmarks and compare them against the baseline.
• Ensure that all functional tests still pass, confirming that the optimizations did not introduce regressions.

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Assignment Request (L3/Core Feature)

I would like to be assigned to this issue. Below is my proposed implementation plan:

Step-by-Step Implementation Plan:

1. Profiling: Introduce temporary performance profiling to identify the exact functions causing the bottleneck.
2. Optimization: Depending on the findings, implement either loop unrolling, memoization of pure functions, or batch processing of state updates.
3. Benchmarking: Create a simple benchmark script that can be run in CI to prevent future performance regressions.
4. Testing: Verify the optimizations against the existing test suite.

Could you also please add the ECSoC26 label?