python-3.x - Windows上的Python中的进程之间的time.perf_counter()是否应保持一致？

更新:已提交对此错误的修复程序，它将在Python 3.10中首次亮相，预计将在2021年10月发布。有关详细信息，请参见bug report。

time.perf_counter() 的文档表明它是系统范围的

我在解释系统范围内的跨流程一致性时是否正确？
如下所示，它在Linux上似乎是一致的，但在Windows上却不一致。此外，Python 3.6的Windows行为与3.7显着不同。
如果有人能指出有关此行为的文档或错误报告，我将不胜感激。
测试用例

import concurrent.futures
import time

def worker():
    return time.perf_counter()

if __name__ == '__main__':
    pool = concurrent.futures.ProcessPoolExecutor()
    futures = []
    for i in range(3):
        print('Submitting worker {:d} at time.perf_counter() == {:.3f}'.format(i, time.perf_counter()))
        futures.append(pool.submit(worker))
        time.sleep(1)

    for i, f in enumerate(futures):
        print('Worker {:d} started at time.perf_counter() == {:.3f}'.format(i, f.result()))

Windows 7上的结果

C:\...>Python36\python.exe -VV
Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)]

C:\...>Python36\python.exe perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 0.000
Submitting worker 1 at time.perf_counter() == 1.169
Submitting worker 2 at time.perf_counter() == 2.170
Worker 0 started at time.perf_counter() == 0.000
Worker 1 started at time.perf_counter() == 0.533
Worker 2 started at time.perf_counter() == 0.000

C:\...>Python37\python.exe -VV
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)]

C:\...>Python37\python.exe perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 0.376
Submitting worker 1 at time.perf_counter() == 1.527
Submitting worker 2 at time.perf_counter() == 2.529
Worker 0 started at time.perf_counter() == 0.380
Worker 1 started at time.perf_counter() == 0.956
Worker 2 started at time.perf_counter() == 1.963

为了简洁起见，我在Windows上省略了进一步的结果，但是在Windows 8.1上也观察到了相同的行为。此外，Python 3.6.7的行为与3.6.8相同，而Python 3.7.1的行为与3.7.3相同。
Ubuntu 18.04.1 LTS上的结果

$ python3 -VV
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0]

$ python3 perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 2075.896
Submitting worker 1 at time.perf_counter() == 2076.900
Submitting worker 2 at time.perf_counter() == 2077.903
Worker 0 started at time.perf_counter() == 2075.900
Worker 1 started at time.perf_counter() == 2076.902
Worker 2 started at time.perf_counter() == 2077.905

$ python3.7 -VV
Python 3.7.1 (default, Oct 22 2018, 11:21:55)
[GCC 8.2.0]

$ python3.7 perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 1692.514
Submitting worker 1 at time.perf_counter() == 1693.518
Submitting worker 2 at time.perf_counter() == 1694.520
Worker 0 started at time.perf_counter() == 1692.517
Worker 1 started at time.perf_counter() == 1693.519
Worker 2 started at time.perf_counter() == 1694.522

最佳答案

在Windows中，time.perf_counter基于WINAPI QueryPerformanceCounter。该计数器是系统范围的。有关更多信息，请参见acquiring high-resolution time stamps。

也就是说，Windows中的perf_counter返回一个相对于进程启动值的值。因此，它不是系统范围的值。这样做是为了减少将整数值转换为只有15位精度的float时的精度损失。在大多数情况下，不需要相对值，只需要微秒的精度即可。应该有一个可选参数来查询真实的QPC计数器值，尤其是对于3.7+中的perf_counter_ns。

关于3.6和3.7中perf_counter返回的不同初始值，实现随时间变化了一点。在3.6.8中，perf_counter是在Modules/timemodule.c中实现的，因此初始值是在首次导入和初始化time模块时存储的，这就是为什么看到第一个结果为0.000秒的原因。在最新版本中，它是在Python的C API中单独实现的。例如，请参阅最新的3.8 beta版中的"Python/pytime.c"。在这种情况下，当Python代码调用time.perf_counter()时，计数器的增量已经远远超过了启动值。

这是基于ctypes的替代实现，它使用系统范围的QPC值而不是相对值。

import sys

if sys.platform != 'win32':
    from time import perf_counter
    try:
        from time import perf_counter_ns
    except ImportError:
        def perf_counter_ns():
            """perf_counter_ns() -> int

            Performance counter for benchmarking as nanoseconds.
            """
            return int(perf_counter() * 10**9)
else:
    import ctypes
    from ctypes import wintypes

    kernel32 = ctypes.WinDLL('kernel32', use_last_error=True)

    kernel32.QueryPerformanceFrequency.argtypes = (
        wintypes.PLARGE_INTEGER,) # lpFrequency

    kernel32.QueryPerformanceCounter.argtypes = (
        wintypes.PLARGE_INTEGER,) # lpPerformanceCount

    _qpc_frequency = wintypes.LARGE_INTEGER()
    if not kernel32.QueryPerformanceFrequency(ctypes.byref(_qpc_frequency)):
        raise ctypes.WinError(ctypes.get_last_error())
    _qpc_frequency = _qpc_frequency.value

    def perf_counter_ns():
        """perf_counter_ns() -> int

        Performance counter for benchmarking as nanoseconds.
        """
        count = wintypes.LARGE_INTEGER()
        if not kernel32.QueryPerformanceCounter(ctypes.byref(count)):
            raise ctypes.WinError(ctypes.get_last_error())
        return (count.value * 10**9) // _qpc_frequency

    def perf_counter():
        """perf_counter() -> float

        Performance counter for benchmarking.
        """
        count = wintypes.LARGE_INTEGER()
        if not kernel32.QueryPerformanceCounter(ctypes.byref(count)):
            raise ctypes.WinError(ctypes.get_last_error())
        return count.value / _qpc_frequency

QPC通常具有0.1微秒的分辨率。 CPython中的float具有15个十进制数字的精度。因此，此perf_counter的实现在QPC分辨率内，可正常运行约3年。

关于python-3.x - Windows上的Python中的进程之间的time.perf_counter()是否应保持一致？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/56502111/