Python 为什么我的numpy代码与线程不并行?
我需要在光栅(矩阵)上为几个点邻域执行一些计算。我的想法是在并行线程中进行这些计算,然后将得到的光栅相加。我的问题是执行似乎不是并行运行的。当我将点数乘以2时,执行时间将延长2倍。我做错了什么Python 为什么我的numpy代码与线程不并行?,python,numpy,python-multithreading,Python,Numpy,Python Multithreading,我需要在光栅(矩阵)上为几个点邻域执行一些计算。我的想法是在并行线程中进行这些计算,然后将得到的光栅相加。我的问题是执行似乎不是并行运行的。当我将点数乘以2时,执行时间将延长2倍。我做错了什么 from threading import Lock, Thread import numpy as np import time SIZE = 1000000 THREADS = 8 my_lock=Lock() results = np.zeros(SIZE,dtype=np.float64) d
from threading import Lock, Thread
import numpy as np
import time
SIZE = 1000000
THREADS = 8
my_lock=Lock()
results = np.zeros(SIZE,dtype=np.float64)
def do_job(j):
global results
s_time = time.time()
print("Starting... "+str(j))
#do some calculations
c_r=np.zeros(SIZE,dtype=np.float64)
for i in range(SIZE):
c_r[i]=np.exp(-0.001*i)
print("\t Calculation at job "+str(j)+" lasted: {:3.3f}".format(time.time()-s_time))
#sum up the results
if my_lock.acquire(blocking=True):
results = np.add(results,c_r)
my_lock.release()
print("\t Job "+str(j)+" lasted: {:3.3f}".format(time.time()-s_time))
def main():
global THREADS
s_time = time.time()
threads=[]
while THREADS>0:
p = Thread(target=do_job,args=(THREADS,))
threads.append(p)
p.start()
THREADS = THREADS-1
print("Start finished after : {:3.3f}".format(time.time()-s_time))
for p in threads:
p.join()
print("Total run diuration: {:3.3f}".format(time.time()-s_time))
if __name__ == "__main__":
main()
当我使用THREADS=4运行代码时,我得到:
Starting... 4
Starting... 3
Starting... 2
Starting... 1
Start finished after : 0.069
Calculation at job 4 lasted: 5.805
Job 4 lasted: 5.887
Calculation at job 3 lasted: 6.230
Job 3 lasted: 6.237
Calculation at job 1 lasted: 6.585
Job 1 lasted: 6.595
Calculation at job 2 lasted: 6.737
Job 2 lasted: 6.738
Total run diuration: 6.760
当我切换到THREADS=8时,执行时间大约增加了一倍:
Starting... 8
Starting... 7
Starting... 6
Starting... 5
Starting... 4
Starting... 3
Starting... 1
Start finished after : 0.182
Starting... 2
Calculation at job 7 lasted: 11.883
Job 7 lasted: 11.939
Calculation at job 8 lasted: 13.096
Job 8 lasted: 13.144
Calculation at job 1 lasted: 13.548
Job 1 lasted: 13.576
Calculation at job 3 lasted: 13.723
Job 3 lasted: 13.748
Calculation at job 2 lasted: 14.231
Job 2 lasted: 14.268
Calculation at job 5 lasted: 14.698
Job 5 lasted: 14.708
Calculation at job 4 lasted: 15.000
Job 4 lasted: 15.015
Calculation at job 6 lasted: 15.133
Job 6 lasted: 15.135
Total run diuration: 15.136
您被全局解释器锁(GIL)击中,请参阅 一次只能有一个“线程”进入解释器。 您的代码主要在Python解释器执行的
for i in range(SIZE)
循环中工作。上下文切换只能在IO操作或调用C函数(释放GIL)时发生。此外,与线程执行的操作相比,在线程之间切换的成本更高。这就是为什么添加更多线程会降低执行速度
根据numpy文档,许多操作都会释放GIL,因此,如果您将操作矢量化,迫使程序在numpy中花费更多时间,则可以从线程中获得优势
见帖子:
尝试从以下位置修改:
for i in range(SIZE):
c_r[i]=np.exp(-0.001*i)
致:
因为python的线程基本上仍然在单个线程中运行。如果您有许多等待时间长的IO操作,但没有计算,那么这将提供优势。改用
多处理
包。这里有一篇文章,谢谢。我没有意识到吉尔。经过您建议的修改,执行情况急剧上升:)
c_r = np.exp(-0.001*np.arange(SIZE))