Add comprehensive tests and optimizations for simulation and UI performance

- Introduced a new benchmark save script to create a stress test scenario with extreme values for simulation performance.
- Added a full UI benchmark script to measure frame rates and performance metrics during UI interactions.
- Implemented tests for coarse generations to ensure population transitions are handled correctly.
- Created tests for extra parameters to validate the loading and application of configuration settings.
- Developed simulation optimization tests to verify the efficiency of population drift and rounding mechanisms.
- Added split optimization tests to ensure accurate allocation and rounding behavior in population management.
- Included startup parameter tests to confirm the correct initialization of global parameters before scene readiness.
- Enhanced the audit rounding ledger to compare selective and full rounding results for consistency.

tests/test_quantity_catalog.gd

@@ -30,6 +30,9 @@ func run() -> void:
             for id in requests: requests[id] = E.multiply(requests[id], E.of("1e40"))
         var limit: Variant = E.multiply(rng.randi_range(0, 600), E.of("1e40")) if sample % 3 == 0 else rng.randi_range(0, 600)
         check(E.eq(Math.allocate_whole(requests, limit), Legacy.allocate_whole(requests, limit)), "Allocation matches legacy with limits, ties and big integers")
+        var whole_requests: Dictionary = {}
+        for id in requests: whole_requests[id] = E.floor_value(E.maximum(0, requests[id]))
+        check(E.eq(Math.allocate_whole(whole_requests, limit), Math.allocate_whole_with_total(whole_requests, limit, Math.sum(whole_requests))), "Known-total allocation matches ordinary allocation")
         var count: Variant = limit
         check(E.eq(Math.split(new_remainders, "population", count, weights), Legacy.split(old_remainders, "population", count, weights)), "Split matches legacy rounding")
         check(E.eq(new_remainders, old_remainders), "Signed remainder debt matches legacy")