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Lee Dogeon
99a63f92aa
Add dependency-free CPU-bound workloads adapted from pyperformance 1.14.0 to cover interpreter, standard-library serialization, regex, numeric, and garbage-collection paths under the existing Criterion and CodSpeed harnesses. Keep inputs deterministic and scale expensive cases for simulation while checking that benchmark execution does not leak interpreter state. Assisted-by: Codex:gpt-5.6-sol
55 lines
1.2 KiB
Python
55 lines
1.2 KiB
Python
# Adapted from python/pyperformance 1.14.0 (float).
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"""
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Artificial, floating point-heavy benchmark originally used by Factor.
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"""
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from math import sin, cos, sqrt
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POINTS = 100000
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class Point(object):
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__slots__ = ('x', 'y', 'z')
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def __init__(self, i):
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self.x = x = sin(i)
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self.y = cos(i) * 3
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self.z = (x * x) / 2
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def __repr__(self):
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return "<Point: x=%s, y=%s, z=%s>" % (self.x, self.y, self.z)
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def normalize(self):
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x = self.x
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y = self.y
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z = self.z
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norm = sqrt(x * x + y * y + z * z)
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self.x /= norm
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self.y /= norm
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self.z /= norm
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def maximize(self, other):
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self.x = self.x if self.x > other.x else other.x
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self.y = self.y if self.y > other.y else other.y
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self.z = self.z if self.z > other.z else other.z
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return self
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def maximize(points):
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next = points[0]
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for p in points[1:]:
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next = next.maximize(p)
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return next
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def benchmark(n):
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points = [None] * n
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for i in range(n):
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points[i] = Point(i)
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for p in points:
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p.normalize()
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return maximize(points)
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result = benchmark(10000)
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assert result.x <= 1.0 and result.y <= 1.0 and result.z <= 1.0
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