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97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
"""
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base:
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Benchmark 1: cargo build --release
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Time (mean ± σ): 58.150 s ± 0.163 s [User: 758.211 s, System: 37.637 s]
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Range (min … max): 57.936 s … 58.321 s 5 runs
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thin:
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Benchmark 1: cargo build --release
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Time (mean ± σ): 63.999 s ± 0.105 s [User: 879.703 s, System: 40.045 s]
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Range (min … max): 63.921 s … 64.182 s 5 runs
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fat:
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Time (mean ± σ): 264.606 s ± 2.238 s [User: 570.800 s, System: 31.826 s]
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Range (min … max): 261.573 s … 267.297 s 5 runs
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# cargo
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base:
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Benchmark 1: cargo build --release
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Time (mean ± σ): 89.381 s ± 0.460 s [User: 689.874 s, System: 55.347 s]
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Range (min … max): 88.605 s … 89.696 s 5 runs
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thin:
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Benchmark 1: cargo build --release
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Time (mean ± σ): 91.208 s ± 0.610 s [User: 757.353 s, System: 58.558 s]
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Range (min … max): 90.415 s … 92.112 s 5 runs
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fat:
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Time (mean ± σ): 212.215 s ± 2.062 s [User: 576.259 s, System: 50.961 s]
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Range (min … max): 208.662 s … 213.818 s 5 runs
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# ripgrep
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base:
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Time (mean ± σ): 7.507 s ± 0.223 s [User: 64.115 s, System: 4.514 s]
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Range (min … max): 7.357 s … 7.882 s 5 runs
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thin:
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Time (mean ± σ): 9.285 s ± 0.019 s [User: 81.101 s, System: 5.241 s]
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Range (min … max): 9.262 s … 9.308 s 5 runs
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fat:
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Time (mean ± σ): 29.202 s ± 0.279 s [User: 51.015 s, System: 3.652 s]
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Range (min … max): 28.860 s … 29.574 s 5 runs
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# triagebot
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base:
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Time (mean ± σ): 74.532 s ± 0.378 s [User: 766.778 s, System: 58.719 s]
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Range (min … max): 74.105 s … 75.109 s 5 runs
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thin:
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Time (mean ± σ): 89.505 s ± 0.299 s [User: 1523.951 s, System: 102.429 s]
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Range (min … max): 89.024 s … 89.796 s 5 runs
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fat:
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Time (mean ± σ): 273.275 s ± 1.694 s [User: 929.604 s, System: 65.856 s]
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Range (min … max): 271.007 s … 275.619 s 5 runs
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"""
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data = [
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{
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"name": "r-a",
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"base": 58.150,
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"thin": 63.999,
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"fat": 264.606,
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},
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{
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"name": "cargo",
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"base": 89.381,
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"thin": 91.208,
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"fat": 212.215,
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},
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{
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"name": "ripgrep",
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"base": 7.507,
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"thin": 9.285,
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"fat": 29.202,
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},
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{
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"name": "triagebot",
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"base": 74.532,
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"thin": 89.505,
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"fat": 273.275,
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}
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]
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for bench in data:
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print(f"{bench["name"]} ThinLTO: {bench["thin"] / bench["base"]}")
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print(f"{bench["name"]} Fat LTO: {bench["fat"] / bench["base"]}")
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def avg_of(scenario: str) -> float:
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avg_percentage = sum([bench[scenario] / bench["base"] for bench in data]) / len(data)
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return avg_percentage
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print(f"ThinLTO: {avg_of("thin")}")
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print(f"Fat LTO: {avg_of("fat")}")
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