Kirill
02c9843e1b
- Implemented runtime-only adaptive quality controller to optimize simulation performance based on CPU time. - Added adaptive quality thresholds and recovery cycles to dynamically adjust simulation fidelity. - Introduced single-step solving method for civilization transitions to improve performance under high loads. - Updated benchmark scripts to include speed parameter for testing various simulation speeds. - Added tests for adaptive quality adjustments and their effects on civilization flow calculations. - Documented results of optimization attempts and their impact on performance metrics.
51 lines
1.9 KiB
GDScript
51 lines
1.9 KiB
GDScript
extends SceneTree
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var checks: int = 0
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var failures: int = 0
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func _initialize() -> void:
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call_deferred("run")
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func check(condition: bool, message: String) -> void:
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checks += 1
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if not condition:
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failures += 1
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push_error(message)
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func run() -> void:
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var world = load("res://world/owniverse/owniverse.gd").new()
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world.update_adaptive_quality(250000)
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check(world.adaptive_quality == 2, "A slow cycle selects the medium approximation")
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world.update_adaptive_quality(250000)
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check(world.adaptive_quality == 3, "A slow medium-approximation cycle selects the coarse mode")
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check(world.adaptive_generation_phase == 1, "Coarse generation phase alternates")
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for i in 36: world.update_adaptive_quality(100000)
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check(world.adaptive_quality == 2, "Thirty consecutive recovery cycles lower quality by one level")
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for i in 30: world.update_adaptive_quality(100000)
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check(world.adaptive_quality == 1, "Sustained headroom lowers quality gradually")
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for i in 30: world.update_adaptive_quality(100000)
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check(world.adaptive_quality == 0, "The exact mode returns after sustained headroom")
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var rows: Array = []
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for node in 12:
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var edges: Dictionary = {}
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var outgoing: float = 0.0
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if node < 6:
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edges[node + 6] = 0.25
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outgoing = 0.25
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rows.append({"edges": edges, "outgoing": outgoing})
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var generator: Dictionary = {"rows": rows, "index": {"source": 0}}
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var solved: Dictionary = world.civilization_flow._solve_single_step(generator, "source", 2.0)
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for ethic in 6:
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var mass: float = 0.0
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var occupation: float = 0.0
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for node in range(ethic, 12, 6):
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mass += solved.mass[node]
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occupation += solved.occupation[node]
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check(absf(mass - 1.0) < 1e-12, "Approximate transition conserves ethic mass %d" % ethic)
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check(absf(occupation - 2.0) < 1e-12, "Approximate transition conserves integrated time %d" % ethic)
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world.free()
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print("Adaptive quality: %d checks, %d failures" % [checks, failures])
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quit(1 if failures else 0)
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