{"id":1019150,"date":"2024-12-27T12:51:42","date_gmt":"2024-12-27T04:51:42","guid":{"rendered":""},"modified":"2024-12-27T12:51:59","modified_gmt":"2024-12-27T04:51:59","slug":"python%e5%a6%82%e4%bd%95%e6%8e%a7%e5%88%b6cpu%e6%a0%b8%e5%bf%83","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1019150.html","title":{"rendered":"python\u5982\u4f55\u63a7\u5236cpu\u6838\u5fc3"},"content":{"rendered":"

\"python\u5982\u4f55\u63a7\u5236cpu\u6838\u5fc3\"<\/p>\n

\u8981\u5728Python\u4e2d\u63a7\u5236CPU\u6838\u5fc3\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff1a\u5229\u7528Python\u7684\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u5e93\u3001\u8bbe\u7f6e\u73af\u5883\u53d8\u91cf\u9650\u5236CPU\u4f7f\u7528\u3001\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93\u8fdb\u884c\u9ad8\u7ea7\u63a7\u5236<\/strong>\u3002\u5176\u4e2d\uff0c\u5229\u7528Python\u7684\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u5e93<\/strong>\u662f\u6bd4\u8f83\u5e38\u7528\u7684\u65b9\u6cd5\u3002\u901a\u8fc7concurrent.futures<\/code>\u6a21\u5757\u7684ProcessPoolExecutor<\/code>\u7c7b\u6216multiprocessing<\/code>\u6a21\u5757\uff0c\u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u7a0b\u5e8f\u4f7f\u7528\u7684CPU\u6838\u5fc3\u6570\u91cf\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5229\u7528concurrent.futures<\/code>\u6a21\u5757<\/h3>\n<\/p>\n

concurrent.futures<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u4e00\u79cd\u9ad8\u7ea7\u7684\u63a5\u53e3\u6765\u5f02\u6b65\u6267\u884c\u51fd\u6570\u8c03\u7528\u3002\u7279\u522b\u662fProcessPoolExecutor<\/code>\u7c7b\u5141\u8bb8\u6211\u4eec\u6307\u5b9a\u8fdb\u7a0b\u6c60\u7684\u5927\u5c0f\uff0c\u4ece\u800c\u63a7\u5236\u4f7f\u7528\u7684CPU\u6838\u5fc3\u6570\u91cf\u3002<\/p>\n<\/p>\n

1.1 \u4f7f\u7528ProcessPoolExecutor<\/code><\/h4>\n<\/p>\n

ProcessPoolExecutor<\/code>\u662fPython\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e00\u79cd\u4fbf\u6377\u7684\u65b9\u5f0f\u6765\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\u3002\u901a\u8fc7\u6307\u5b9amax_workers<\/code>\u53c2\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u63a7\u5236\u8fdb\u7a0b\u6c60\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n

from concurrent.futures import ProcessPoolExecutor<\/p>\n
import os<\/p>\n
def task(n):<\/p>\n
    print(f"Running task {n} on process id {os.getpid()}")<\/p>\n
    return n * n<\/p>\n
if __name__ == "__mAI<\/a>n__":<\/p>\n
    with ProcessPoolExecutor(max_workers=4) as executor:<\/p>\n
        results = executor.map(task, range(10))<\/p>\n
        for result in results:<\/p>\n
            print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0cmax_workers=4<\/code>\u9650\u5236\u4e86\u6700\u591a\u540c\u65f6\u4f7f\u75284\u4e2aCPU\u6838\u5fc3\u3002executor.map()<\/code>\u65b9\u6cd5\u4f1a\u5c06\u4efb\u52a1\u5206\u53d1\u7ed9\u5404\u4e2a\u8fdb\u7a0b\u5e76\u884c\u6267\u884c\u3002<\/p>\n<\/p>\n
1.2 \u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n<\/p>\n
\u4f7f\u7528ProcessPoolExecutor<\/code>\u7684\u4f18\u52bf\u5728\u4e8e\u5176\u7b80\u5355\u6613\u7528\uff0c\u9002\u5408\u5927\u591a\u6570\u9700\u8981\u5e76\u884c\u8ba1\u7b97\u7684\u573a\u666f\u3002\u7136\u800c\uff0c\u5b83\u7684\u5c40\u9650\u5728\u4e8ePython\u7684\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08GIL\uff09\u4f1a\u9650\u5236\u7ebf\u7a0b\u7684\u5e76\u884c\u6027\uff0c\u56e0\u6b64\u5bf9\u4e8eCPU\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u4f7f\u7528\u591a\u8fdb\u7a0b\u800c\u975e\u591a\u7ebf\u7a0b\u66f4\u4e3a\u5408\u9002\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528multiprocessing<\/code>\u6a21\u5757<\/h3>\n<\/p>\n
multiprocessing<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u66f4\u4f4e\u7ea7\u522b\u7684\u63a7\u5236\uff0c\u53ef\u4ee5\u76f4\u63a5\u521b\u5efa\u8fdb\u7a0b\u5e76\u6307\u5b9a\u5176\u4f7f\u7528\u7684CPU\u6838\u5fc3\u6570\u91cf\u3002<\/p>\n<\/p>\n
2.1 \u57fa\u672c\u7528\u6cd5<\/h4>\n<\/p>\n
\u901a\u8fc7multiprocessing<\/code>\u6a21\u5757\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u8fdb\u7a0b\u5e76\u6307\u5b9a\u6bcf\u4e2a\u8fdb\u7a0b\u6267\u884c\u7684\u4efb\u52a1\u3002\u901a\u8fc7cpu_count()<\/code>\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u53d6\u7cfb\u7edf\u7684CPU\u6838\u5fc3\u6570\u91cf\uff0c\u5e76\u6839\u636e\u9700\u8981\u8fdb\u884c\u5206\u914d\u3002<\/p>\n<\/p>\n
from multiprocessing import Process, cpu_count<\/p>\n
def worker(num):<\/p>\n
    print(f'Worker: {num}')<\/p>\n
if __name__ == '__main__':<\/p>\n
    num_cores = cpu_count()<\/p>\n
    print(f"Number of CPU cores: {num_cores}")<\/p>\n
    processes = []<\/p>\n
    for i in range(num_cores):<\/p>\n
        p = Process(target=worker, args=(i,))<\/p>\n
        processes.append(p)<\/p>\n
        p.start()<\/p>\n
    for p in processes:<\/p>\n
        p.join()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e0eCPU\u6838\u5fc3\u6570\u91cf\u76f8\u540c\u7684\u8fdb\u7a0b\u6570\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u6267\u884c\u4e00\u4e2a\u7b80\u5355\u7684\u4efb\u52a1\u3002<\/p>\n<\/p>\n
2.2 \u8fdb\u9636\u7528\u6cd5<\/h4>\n<\/p>\n
multiprocessing<\/code>\u6a21\u5757\u8fd8\u63d0\u4f9b\u4e86Pool<\/code>\u7c7b\uff0c\u5141\u8bb8\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u8fdb\u7a0b\u6c60\u5e76\u901a\u8fc7apply_async()<\/code>\u6216map()<\/code>\u65b9\u6cd5\u5206\u914d\u4efb\u52a1\u3002<\/p>\n<\/p>\n
from multiprocessing import Pool<\/p>\n
def square(x):<\/p>\n
    return x * x<\/p>\n
if __name__ == '__main__':<\/p>\n
    with Pool(processes=4) as pool:<\/p>\n
        results = pool.map(square, range(10))<\/p>\n
        print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Pool<\/code>\u6765\u521b\u5efa\u4e00\u4e2a\u5305\u542b4\u4e2a\u8fdb\u7a0b\u7684\u8fdb\u7a0b\u6c60\uff0c\u5e76\u901a\u8fc7map()<\/code>\u65b9\u6cd5\u5c06\u4efb\u52a1\u5206\u914d\u7ed9\u8fd9\u4e9b\u8fdb\u7a0b\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u901a\u8fc7\u73af\u5883\u53d8\u91cf\u9650\u5236CPU\u4f7f\u7528<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u5e0c\u671b\u901a\u8fc7\u73af\u5883\u53d8\u91cf\u9650\u5236Python\u7a0b\u5e8f\u4f7f\u7528\u7684CPU\u6838\u5fc3\u6570\u91cf\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eOMP_NUM_THREADS<\/code>\u6216MKL_NUM_THREADS<\/code>\u7b49\u73af\u5883\u53d8\u91cf\u5b9e\u73b0\uff0c\u7279\u522b\u662f\u5728\u4f7f\u7528\u6570\u503c\u8ba1\u7b97\u5e93\uff08\u5982NumPy\u3001SciPy\uff09\u65f6\u3002<\/p>\n<\/p>\n
3.1 \u8bbe\u7f6e\u73af\u5883\u53d8\u91cf<\/h4>\n<\/p>\n
\u53ef\u4ee5\u5728\u8fd0\u884cPython\u7a0b\u5e8f\u4e4b\u524d\uff0c\u901a\u8fc7\u547d\u4ee4\u884c\u8bbe\u7f6e\u73af\u5883\u53d8\u91cf\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
export OMP_NUM_THREADS=4<\/p>\n
export MKL_NUM_THREADS=4<\/p>\n
python your_script.py<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e9b\u73af\u5883\u53d8\u91cf\u4f1a\u544a\u8bc9\u5e95\u5c42\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\u9650\u5236\u4f7f\u7528\u7684\u7ebf\u7a0b\u6570\uff0c\u4ece\u800c\u95f4\u63a5\u63a7\u5236CPU\u6838\u5fc3\u7684\u4f7f\u7528\u3002<\/p>\n<\/p>\n
3.2 \u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n<\/p>\n
\u901a\u8fc7\u73af\u5883\u53d8\u91cf\u9650\u5236CPU\u4f7f\u7528\u7684\u4f18\u52bf\u5728\u4e8e\u7b80\u5355\u6709\u6548\uff0c\u7279\u522b\u9002\u5408\u4e8e\u4f7f\u7528\u6570\u503c\u8ba1\u7b97\u5e93\u7684\u573a\u666f\u3002\u7136\u800c\uff0c\u8fd9\u79cd\u65b9\u6cd5\u7684\u5c40\u9650\u5728\u4e8e\u5b83\u53ea\u80fd\u63a7\u5236\u7279\u5b9a\u5e93\u7684\u5e76\u884c\u884c\u4e3a\uff0c\u800c\u65e0\u6cd5\u76f4\u63a5\u63a7\u5236Python\u7684\u8fdb\u7a0b\u6216\u7ebf\u7a0b\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93\u8fdb\u884c\u9ad8\u7ea7\u63a7\u5236<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u9700\u8981\u66f4\u9ad8\u7ea7\u63a7\u5236\u7684\u573a\u666f\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93\uff0c\u5982psutil<\/code>\u6216numba<\/code>\u3002<\/p>\n<\/p>\n
4.1 \u4f7f\u7528psutil<\/code>\u9650\u5236CPU\u4f7f\u7528<\/h4>\n<\/p>\n
psutil<\/code>\u662f\u4e00\u4e2a\u8de8\u5e73\u53f0\u5e93\uff0c\u63d0\u4f9b\u4e86\u5bf9\u7cfb\u7edf\u786c\u4ef6\uff08\u5982CPU\u3001\u5185\u5b58\uff09\u8fdb\u884c\u76d1\u63a7\u7684\u529f\u80fd\u3002\u901a\u8fc7psutil<\/code>\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u53d6\u5f53\u524d\u8fdb\u7a0b\u7684CPU\u4f7f\u7528\u60c5\u51b5\uff0c\u5e76\u8fdb\u884c\u9650\u5236\u3002<\/p>\n<\/p>\n
import psutil<\/p>\n
def limit_cpu_usage():<\/p>\n
    p = psutil.Process()<\/p>\n
    p.cpu_affinity([0, 1])  # \u9650\u5236\u8fdb\u7a0b\u4f7f\u7528\u7684CPU\u6838\u5fc3\u4e3a0\u548c1<\/p>\n
if __name__ == "__main__":<\/p>\n
    limit_cpu_usage()<\/p>\n
    # \u5176\u4ed6\u8ba1\u7b97\u4efb\u52a1<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7cpu_affinity()<\/code>\u65b9\u6cd5\u9650\u5236\u5f53\u524d\u8fdb\u7a0b\u4f7f\u7528\u7279\u5b9a\u7684CPU\u6838\u5fc3\u3002<\/p>\n<\/p>\n
4.2 \u4f7f\u7528numba<\/code>\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97<\/h4>\n<\/p>\n
numba<\/code>\u662f\u4e00\u4e2a\u7528\u4e8e\u52a0\u901f\u6570\u503c\u8ba1\u7b97\u7684Python\u7f16\u8bd1\u5668\u3002\u901a\u8fc7numba<\/code>\u7684@jit<\/code>\u88c5\u9970\u5668\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
from numba import jit, prange<\/p>\n
@jit(nopython=True, parallel=True)<\/p>\n
def parallel_sum(arr):<\/p>\n
    sum = 0<\/p>\n
    for i in prange(len(arr)):<\/p>\n
        sum += arr[i]<\/p>\n
    return sum<\/p>\n
if __name__ == "__main__":<\/p>\n
    arr = [i for i in range(1000000)]<\/p>\n
    result = parallel_sum(arr)<\/p>\n
    print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cparallel=True<\/code>\u53c2\u6570\u4f7f\u5f97numba<\/code>\u5c1d\u8bd5\u5e76\u884c\u6267\u884c\u5faa\u73af\uff0c\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u63a7\u5236CPU\u6838\u5fc3\u7684\u4f7f\u7528\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u4e0d\u540c\u7684\u65b9\u6cd5\uff1a\u5bf9\u4e8e\u7b80\u5355\u7684\u5e76\u884c\u4efb\u52a1\uff0c\u4f7f\u7528concurrent.futures<\/code>\u6216multiprocessing<\/code>\u6a21\u5757\u5373\u53ef\uff1b\u82e5\u9700\u8981\u5728\u6570\u503c\u8ba1\u7b97\u4e2d\u9650\u5236CPU\u4f7f\u7528\uff0c\u53ef\u4ee5\u901a\u8fc7\u73af\u5883\u53d8\u91cf\u8fdb\u884c\u8bbe\u7f6e\uff1b\u800c\u5bf9\u4e8e\u66f4\u9ad8\u7ea7\u7684\u63a7\u5236\u9700\u6c42\uff0c\u53ef\u4ee5\u501f\u52a9psutil<\/code>\u6216numba<\/code>\u7b49\u7b2c\u4e09\u65b9\u5e93<\/strong>\u3002\u6839\u636e\u4efb\u52a1\u7684\u6027\u8d28\u548c\u590d\u6742\u6027\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u4ee5\u8fbe\u5230\u6700\u4f73\u7684\u6027\u80fd\u548c\u8d44\u6e90\u5229\u7528\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5408\u7406\u5730\u63a7\u5236CPU\u6838\u5fc3\u7684\u4f7f\u7528\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6548\u7387\uff0c\u8fd8\u53ef\u4ee5\u907f\u514d\u8d44\u6e90\u7684\u6d6a\u8d39\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u9ad8\u6548\u7684\u8ba1\u7b97\u4efb\u52a1\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u4efb\u52a1\uff0c\u8fd8\u662f\u590d\u6742\u7684\u5e76\u884c\u6570\u503c\u8ba1\u7b97\uff0cPython\u90fd\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u5e93\u6765\u5e2e\u52a9\u5f00\u53d1\u8005\u5b9e\u73b0\u76ee\u6807\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u6765\u7ba1\u7406CPU\u6838\u5fc3\u7684\u4f7f\u7528\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528os<\/code>\u548cpsutil<\/code>\u5e93\u6765\u7ba1\u7406\u548c\u76d1\u63a7CPU\u6838\u5fc3\u7684\u4f7f\u7528\u60c5\u51b5\u3002psutil<\/code>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7cfb\u7edf\u548c\u8fdb\u7a0b\u7ba1\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u83b7\u53d6CPU\u6838\u5fc3\u7684\u5229\u7528\u7387\uff0c\u5e76\u8fdb\u884c\u4e00\u4e9b\u63a7\u5236\uff0c\u6bd4\u5982\u8bbe\u7f6e\u8fdb\u7a0b\u7684CPU\u4eb2\u548c\u6027\uff0c\u9650\u5236\u67d0\u4e2a\u8fdb\u7a0b\u4f7f\u7528\u7684CPU\u6838\u5fc3\u3002<\/p>\n
\u4f7f\u7528Python\u65f6\uff0c\u5982\u4f55\u4f18\u5316\u7a0b\u5e8f\u4ee5\u51cf\u5c11CPU\u6838\u5fc3\u7684\u5360\u7528\uff1f<\/strong>
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