首页 > 解决方案 > 如何使用 Numba 为 Python 中的线程释放 GIL?

问题描述

我想制作一个由两部分组成的程序:一个是接收数据,另一个是将其写入文件。我认为如果我可以使用 2 个线程(可能还有 2 个 cpu 内核)分别完成工作会更好。我发现了这个:https ://numba.pydata.org/numba-doc/dev/user/jit.html#compilation-options它允许您释放 GIL。我想知道它是否适合我的目的,我是否可以将它用于这种工作。这是我尝试过的:

import threading
import time
import os
import queue
import numba
import numpy as np

condition = threading.Condition()
q_text = queue.Queue()

#@numba.jit(nopython=True, nogil=True)
def consumer():
    t = threading.currentThread()

    with condition:
        while True:
            str_test = q_text.get()
            with open('hello.txt', 'a') as f:
                f.write(str_test)
            condition.wait()            

def sender():
    with condition:
        condition.notifyAll()

def add_q(arr="hi\n"):
    q_text.put(arr)
    sender()

c1 = threading.Thread(name='c1', target=consumer)
c1.start()

add_q()

没有它可以正常工作numba,但是当我将它应用于 时consumer,它给了我一个错误:

Exception in thread c1:
Traceback (most recent call last):
  File "d:\python36-32\lib\threading.py", line 916, in _bootstrap_inner
    self.run()
  File "d:\python36-32\lib\threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)
  File "d:\python36-32\lib\site-packages\numba\dispatcher.py", line 368, in _compile_for_args
    raise e
  File "d:\python36-32\lib\site-packages\numba\dispatcher.py", line 325, in _compile_for_args
    return self.compile(tuple(argtypes))
  File "d:\python36-32\lib\site-packages\numba\dispatcher.py", line 653, in compile
    cres = self._compiler.compile(args, return_type)
  File "d:\python36-32\lib\site-packages\numba\dispatcher.py", line 83, in compile
    pipeline_class=self.pipeline_class)
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 873, in compile_extra
    return pipeline.compile_extra(func)
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 367, in compile_extra
    return self._compile_bytecode()
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 804, in _compile_bytecode
    return self._compile_core()
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 791, in _compile_core
    res = pm.run(self.status)
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 253, in run
    raise patched_exception
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 245, in run
    stage()
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 381, in stage_analyze_bytecode
    func_ir = translate_stage(self.func_id, self.bc)
  File "d:\python36-32\lib\site-packages\numba\compiler.py", line 937, in translate_stage
    return interp.interpret(bytecode)
  File "d:\python36-32\lib\site-packages\numba\interpreter.py", line 92, in interpret
    self.cfa.run()
  File "d:\python36-32\lib\site-packages\numba\controlflow.py", line 515, in run
    assert not inst.is_jump, inst
AssertionError: Failed at nopython (analyzing bytecode)
SETUP_WITH(arg=60, lineno=17)

condition(threading.Condion)如果我从中排除没有错误consumer,所以也许是因为 JIT 没有解释它?我想知道我是否可以采用numba这种目的以及如何解决这个问题(如果可能的话)。

标签: pythonpython-multithreadingnumba

解决方案


您不能threading在 Numba 函数中使用该模块,也不支持打开/写入文件。当您需要计算性能时,Numba 非常棒,您的示例纯粹是 I/O,这不是 Numba 的用例。

Numba 添加内容的唯一方法是对str_test数据应用函数。编译该函数nogil=True将允许多线程。但同样,只有当您与 I/O 相比,该函数的计算成本更高时,这才是值得的。

您可以研究一个更适合 I/O 绑定性能的异步解决方案。

有关线程提高性能的情况,请参阅 Numba 文档中的此示例: https ://numba.pydata.org/numba-doc/dev/user/examples.html#multi-threading


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