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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
43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
# Adapted from python/pyperformance 1.14.0 (decimal_factorial).
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"""
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Calculate `factorial` using the decimal module.
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- 2024-06-14: Michael Droettboom copied this from
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Modules/_decimal/tests/bench.py in the CPython source and adapted to use
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pyperf.
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"""
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# Original copyright notice in CPython source:
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#
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# Copyright (C) 2001-2012 Python Software Foundation. All Rights Reserved.
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# Modified and extended by Stefan Krah.
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#
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import decimal
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def factorial(n, m):
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if n > m:
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return factorial(m, n)
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elif m == 0:
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return 1
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elif n == m:
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return n
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else:
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return factorial(n, (n + m) // 2) * factorial((n + m) // 2 + 1, m)
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def bench_decimal_factorial():
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# The upstream 10,000! and 100,000! inputs are reduced for simulation,
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# while retaining enough precision to exercise large Decimal arithmetic.
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with decimal.localcontext() as context:
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context.prec = 3000
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result = factorial(decimal.Decimal(1000), 0)
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assert len(result.as_tuple().digits) == 2568
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bench_decimal_factorial()
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