python并行操作(基于concurrent.futures.ThreadPoolExecutor)

此为利用cpu并行计算的能力，充分利用cpu在循环时并行计算。其实也是受C++并行操作的影响，如果需要C++版，可以移步C++thread并行笔记

一、明确自身cpu可并行的核数

max_workers = os.cpu_count()

二、根据所有任务计算在各个核上平均跑多少任务

use_cpu_pre_task = all_task_size // max_workers

三、最后把任务划分在不同的核上跑

def process_function(range_rask, arg1, arg2):for i in range(ranges.start, ranges.stop):XXXwith concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:for i in range(max_workers):start_idx = i * use_cpu_pre_taskend_idx = all_task_size if i == max_workers - 1 else (i + 1) * use_cpu_pre_taskrange_task = range(start_idx, end_idx)executor.submit(process_function, range_task, arg1, arg2)

四、拿来主义

def process_function(range_rask, arg1, arg2):for i in range(ranges.start, ranges.stop):XXXif __name__ == "__main__":max_workers = os.cpu_count()use_cpu_pre_task = all_task_size // max_workerswith concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:for i in range(max_workers):start_idx = i * use_cpu_pre_taskend_idx = all_task_size if i == max_workers - 1 else (i + 1) * use_cpu_pre_taskrange_task = range(start_idx, end_idx)executor.submit(process_function, range_task, arg1, arg2)

参考链接：concurrent.futures — 启动并行任务