category

language_learning/python/2020.06.01_parallel_calculations_with_python/application_of_parallel_calculations_with_python.py

@@ -0,0 +1,35 @@
+from multiprocessing import Process
+import os
+import time
+import numpy as np
+import guan
+
+def main(parameter_array, task_index):
+    print ('Process id = %s' % (os.getpid()))
+    result_array = []
+    for parameter in parameter_array:
+        result = parameter*2
+        result_array.append(result)
+    time.sleep(np.random.uniform(1,10))
+    guan.write_one_dimensional_data(parameter_array, result_array, filename='task_index='+str(task_index))
+
+if __name__ == '__main__':
+    cpus = 4
+    parameter_array_all = np.arange(0, 17, 1) 
+    start_time = time.perf_counter()
+    process_array = []
+    for task_index in range(cpus):
+        parameter_array = guan.preprocess_for_parallel_calculations(parameter_array_all, cpus, task_index)
+        process_array.append(Process(target=main, args=(parameter_array, task_index)))
+    for process in process_array: # 运行子进程
+        process.start()
+    for process in process_array: # 等待子进程完成
+        process.join() 
+    end_time = time.perf_counter()
+    print('运行时间=', (end_time-start_time), '\n')
+    f = open('result.txt', 'w')
+    for task_index in range(cpus):
+        with open('task_index='+str(task_index)+'.txt', 'r') as f0:
+            text = f0.read()
+        f.write(text)
+    f.close()

language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py

@@ -0,0 +1,42 @@
+from multiprocessing import Process
+import os
+import time
+
+def run_proc(name): # 要执行的代码
+    start_time = time.perf_counter()
+    time.sleep(2)
+    end_time = time.perf_counter()
+    print ('Process id running on %s = %s' % (name, os.getpid()), '; running time = %s' % (end_time-start_time))
+
+
+if __name__ == '__main__':
+
+    # 串行
+    print('串行程序')
+    print('Process id = %s.' % os.getpid())
+    start_time = time.perf_counter()
+    run_proc('job1')
+    run_proc('job2')
+    run_proc('job3')
+    run_proc('job4')
+    end_time = time.perf_counter()
+    print('CPU执行时间(s)=', (end_time-start_time), '\n')
+
+    # 并行
+    print('并行程序')
+    print('Process id = %s.' % os.getpid())
+    start_time = time.perf_counter()
+    p1 = Process(target=run_proc, args=('job1',))
+    p2 = Process(target=run_proc, args=('job2',))
+    p3 = Process(target=run_proc, args=('job3',))
+    p4 = Process(target=run_proc, args=('job4',))
+    p1.start()
+    p2.start()
+    p3.start()
+    p4.start()
+    p1.join()  # join()方法可以等待子进程结束后再继续往下运行
+    p2.join()  
+    p3.join()  
+    p4.join()
+    end_time = time.perf_counter()
+    print('运行时间(s)=', (end_time-start_time))

language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python_using_Value.py

@@ -0,0 +1,16 @@
+from multiprocessing import Process, Value
+
+def run_proc(name, a, num): # 要执行的代码
+    num.value = a
+
+if __name__ == '__main__':
+    num1 = Value('d', 0.0)  # 共享内存
+    num2 = Value('d', 0.0)  # 共享内存
+    p1 = Process(target=run_proc, args=('job1', 100, num1))
+    p2 = Process(target=run_proc, args=('job2', 200, num2))
+    p1.start()
+    p2.start()
+    p1.join()
+    p2.join() 
+    print(num1.value)
+    print(num2.value)