githubmirror/cpython

Tools/lockbench/lockbench.py
92 строки4 KB
Sam Gross
gh-145230: Update lockbench (gh-145231)
27 фев 2026, 22:09
27 фев 2026, 22:0972eca2a
# Measure the performance of PyMutex locks with short critical sections.
#
# Usage: python Tools/lockbench/lockbench.py [options]
#
# Options:
#   --work-inside N    Units of work inside the critical section (default: 1).
#   --work-outside N   Units of work outside the critical section (default: 0).
#                      Each unit of work is a dependent floating-point
#                      addition, which takes about 0.4 ns on modern
#                      Intel / AMD processors.
#   --num-locks N      Number of independent locks (default: 1). Threads are
#                      assigned to locks round-robin.
#   --random-locks     Each thread picks a random lock per acquisition instead
#                      of using a fixed assignment. Requires --num-locks > 1.
#   --acquisitions N   Lock acquisitions per loop iteration (default: 1).
#   --total-iters N    Fixed iterations per thread (default: 0 = time-based).
#                      Useful for measuring fairness: the benchmark runs until
#                      the slowest thread finishes.
#
# How to interpret the results:
#
# Acquisitions (kHz): Reports the total number of lock acquisitions in
# thousands of acquisitions per second. This is the most important metric,
# particularly for the 1 thread case because even in multithreaded programs,
# most lock acquisitions are not contended. Values for 2+ threads are
# only meaningful for `--disable-gil` builds, because the GIL prevents most
# situations where there is lock contention with short critical sections.
#
# Fairness: A measure of how evenly the lock acquisitions are distributed.
# A fairness of 1.0 means that all threads acquired the lock the same number
# of times. A fairness of 1/N means that only one thread ever acquired the
# lock.
# See https://en.wikipedia.org/wiki/Fairness_measure#Jain's_fairness_index

from _testinternalcapi import benchmark_locks
import argparse


def parse_threads(value):
    if '-' in value:
        lo, hi = value.split('-', 1)
        lo, hi = int(lo), int(hi)
        return range(lo, hi + 1)
    return range(int(value), int(value) + 1)

def jains_fairness(values):
    # Jain's fairness index
    # See https://en.wikipedia.org/wiki/Fairness_measure
    return (sum(values) ** 2) / (len(values) * sum(x ** 2 for x in values))

def main():
    parser = argparse.ArgumentParser(description="Benchmark PyMutex locks")
    parser.add_argument("--work-inside", type=int, default=1,
                        help="units of work inside the critical section")
    parser.add_argument("--work-outside", type=int, default=0,
                        help="units of work outside the critical section")
    parser.add_argument("--acquisitions", type=int, default=1,
                        help="lock acquisitions per loop iteration")
    parser.add_argument("--total-iters", type=int, default=0,
                        help="fixed iterations per thread (0 = time-based)")
    parser.add_argument("--num-locks", type=int, default=1,
                        help="number of independent locks (round-robin assignment)")
    parser.add_argument("--random-locks", action="store_true",
                        help="pick a random lock per acquisition")
    parser.add_argument("threads", type=parse_threads, nargs='?',
                        default=range(1, 11),
                        help="Number of threads: N or MIN-MAX (default: 1-10)")
    args = parser.parse_args()

    header = f"{'Threads': <10}{'Acq (kHz)': >12}{'Fairness': >10}"
    if args.total_iters:
        header += f"{'Wall (ms)': >12}"
    print(header)
    for num_threads in args.threads:
        acquisitions, thread_iters, elapsed_ns = \
            benchmark_locks(
                num_threads, args.work_inside, args.work_outside,
                1000, args.acquisitions, args.total_iters,
                args.num_locks, args.random_locks)

        wall_ms = elapsed_ns / 1e6
        acquisitions /= 1000  # report in kHz for readability
        fairness = jains_fairness(thread_iters)

        line = f"{num_threads: <10}{acquisitions: >12.0f}{fairness: >10.2f}"
        if args.total_iters:
            line += f"{wall_ms: >12.1f}"
        print(line)


if __name__ == "__main__":
    main()