我正在生成 100 个大小为 1000x1000
的随机整数矩阵。我正在使用多处理模块来计算 100 个矩阵的特征值。
代码如下:
import timeit
import numpy as np
import multiprocessing as mp
def calEigen():
S, U = np.linalg.eigh(a)
def multiprocess(processes):
pool = mp.Pool(processes=processes)
#Start timing here as I don't want to include time taken to initialize the processes
start = timeit.default_timer()
results = [pool.apply_async(calEigen, args=())]
stop = timeit.default_timer()
print (processes":", stop - start)
results = [p.get() for p in results]
results.sort() # to sort the results
if __name__ == "__main__":
global a
a=[]
for i in range(0,100):
a.append(np.random.randint(1,100,size=(1000,1000)))
#Print execution time without multiprocessing
start = timeit.default_timer()
calEigen()
stop = timeit.default_timer()
print stop - start
#With 1 process
multiprocess(1)
#With 2 processes
multiprocess(2)
#With 3 processes
multiprocess(3)
#With 4 processes
multiprocess(4)
输出是
0.510247945786
('Process:', 1, 5.1021575927734375e-05)
('Process:', 2, 5.698204040527344e-05)
('Process:', 3, 8.320808410644531e-05)
('Process:', 4, 7.200241088867188e-05)
另一个迭代显示了这个输出:
69.7296020985
('Process:', 1, 0.0009050369262695312)
('Process:', 2, 0.023727893829345703)
('Process:', 3, 0.0003509521484375)
('Process:', 4, 0.057518959045410156)
我的问题是:
- 为什么执行时间不随着次数的增加而减少 进程增加?我是否正确使用了多处理模块?
- 我计算的执行时间是否正确?
我已经编辑了下面评论中给出的代码。我希望串行和多处理函数为同一个 100 个矩阵列表找到特征值。编辑后的代码是-
import numpy as np
import time
from multiprocessing import Pool
a=[]
for i in range(0,100):
a.append(np.random.randint(1,100,size=(1000,1000)))
def serial(z):
result = []
start_time = time.time()
for i in range(0,100):
result.append(np.linalg.eigh(z[i])) #calculate eigen values and append to result list
end_time = time.time()
print("Single process took :", end_time - start_time, "seconds")
def caleigen(c):
result = []
result.append(np.linalg.eigh(c)) #calculate eigenvalues and append to result list
return result
def mp(x):
start_time = time.time()
with Pool(processes=x) as pool: # start a pool of 4 workers
result = pool.map_async(caleigen,a) # distribute work to workers
result = result.get() # collect result from MapResult object
end_time = time.time()
print("Mutltiprocessing took:", end_time - start_time, "seconds" )
if __name__ == "__main__":
serial(a)
mp(1,a)
mp(2,a)
mp(3,a)
mp(4,a)
随着进程数量的增加,时间并没有减少。我哪里错了? multiprocessing 是否将列表划分为进程的 block ,还是我必须进行划分?
最佳答案
您没有正确使用多处理模块。正如@dopstar 指出的那样,您没有划分任务。进程池只有一个任务,所以无论你分配多少 worker ,只有一个会得到这份工作。至于你的第二个问题,我没有使用 timeit
来精确测量处理时间。我只是使用 time
模块来大致了解事物的速度。不过,它在大多数情况下都能达到目的。如果我理解您要正确执行的操作,那么这应该是您代码的单进程版本
import numpy as np
import time
result = []
start_time = time.time()
for i in range(100):
a = np.random.randint(1, 100, size=(1000,1000)) #generate random matrix
result.append(np.linalg.eigh(a)) #calculate eigen values and append to result list
end_time = time.time()
print("Single process took :", end_time - start_time, "seconds")
单进程版本在我的电脑上花费了 15.27 秒。下面是多进程版本,在我的电脑上只用了 0.46 秒。我还包括了单进程版本以供比较。 (单进程版本也必须包含在 if
block 中,并放在多进程版本之后。)因为你想重复计算 100 次,这样会容易得多创建一个工作池并让他们自动承担未完成的任务,而不是手动启动每个进程并指定每个进程应该做什么。在我的代码中,caleigen
调用的参数只是为了跟踪任务已执行了多少次。最后,map_async
通常比 apply_async
更快,其缺点是消耗的内存稍多,并且函数调用仅采用一个参数。使用 map_async
而不是 map
的原因是,在这种情况下,返回结果的顺序无关紧要,而且 map_async
更快比 map
。
from multiprocessing import Pool
import numpy as np
import time
def caleigen(x): # define work for each worker
a = np.random.randint(1,100,size=(1000,1000))
S, U = np.linalg.eigh(a)
return S, U
if __name__ == "main":
start_time = time.time()
with Pool(processes=4) as pool: # start a pool of 4 workers
result = pool.map_async(caleigen, range(100)) # distribute work to workers
result = result.get() # collect result from MapResult object
end_time = time.time()
print("Mutltiprocessing took:", end_time - start_time, "seconds" )
# Run the single process version for comparison. This has to be within the if block as well.
result = []
start_time = time.time()
for i in range(100):
a = np.random.randint(1, 100, size=(1000,1000)) #generate random matrix
result.append(np.linalg.eigh(a)) #calculate eigen values and append to result list
end_time = time.time()
print("Single process took :", end_time - start_time, "seconds")
关于python - 用于计算特征值的多处理,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33462264/