[英]Why is my numpy code with threading not parallel?
我需要对几个点邻域的栅格(矩阵)执行一些计算。 我的想法是在并行线程中进行这些计算,然后总结生成的栅格。 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,执行时间延长了 2 倍。 我究竟做错了什么?
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)
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,执行时间大约加倍:
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 Global Interpreter Lock (GIL) 击中,请参阅https://wiki.python.org/moin/GlobalInterpreterLock 。
当时只有一个“线程”可以进入解释器。 您的代码主要在for i in range(SIZE)
循环内工作,该循环由 Python 解释器执行。 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,线程之间的切换成本较大。 这就是为什么添加更多线程会减慢执行速度。
根据 numpy 文档,许多操作都发布了 GIL,因此,如果您将操作向量化,迫使程序在 numpy 中花费更多时间,则您可以从线程中获得优势。
请参阅帖子: 为什么 numpy 计算不受全局解释器锁的影响?
尝试修改:
for i in range(SIZE):
c_r[i]=np.exp(-0.001*i)
到:
c_r = np.exp(-0.001*np.arange(SIZE))
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.