为什么ctypes将Python列表转换为C数组这么慢？

Question

我的代码的瓶颈目前是使用ctypes从Python列表转换为C数组,如本问题所述.

一个小实验表明,与其他Python指令相比,它确实非常慢:

import timeit
setup="from array import array; import ctypes; t = [i for i in range(1000000)];"
print(timeit.timeit(stmt='(ctypes.c_uint32 * len(t))(*t)',setup=setup,number=10))
print(timeit.timeit(stmt='array("I",t)',setup=setup,number=10))
print(timeit.timeit(stmt='set(t)',setup=setup,number=10))

得到:

1.790962941000089
0.0911122129996329
0.3200237319997541

我用CPython 3.4.2获得了这些结果.我和CPython 2.7.9和Pypy 2.4.0的时间相似.

我尝试运行上面的代码perf,注释timeit指令一次只运行一个.我得到这些结果:

ctypes的

 Performance counter stats for 'python3 perf.py':

       1807,891637      task-clock (msec)         #    1,000 CPUs utilized          
                 8      context-switches          #    0,004 K/sec                  
                 0      cpu-migrations            #    0,000 K/sec                  
            59 523      page-faults               #    0,033 M/sec                  
     5 755 704 178      cycles                    #    3,184 GHz                    
    13 552 506 138      instructions              #    2,35  insn per cycle         
     3 217 289 822      branches                  # 1779,581 M/sec                  
           748 614      branch-misses             #    0,02% of all branches        

       1,808349671 seconds time elapsed

排列

 Performance counter stats for 'python3 perf.py':

        144,678718      task-clock (msec)         #    0,998 CPUs utilized          
                 0      context-switches          #    0,000 K/sec                  
                 0      cpu-migrations            #    0,000 K/sec                  
            12 913      page-faults               #    0,089 M/sec                  
       458 284 661      cycles                    #    3,168 GHz                    
     1 253 747 066      instructions              #    2,74  insn per cycle         
       325 528 639      branches                  # 2250,011 M/sec                  
           708 280      branch-misses             #    0,22% of all branches        

       0,144966969 seconds time elapsed

组

 Performance counter stats for 'python3 perf.py':

        369,786395      task-clock (msec)         #    0,999 CPUs utilized          
                 0      context-switches          #    0,000 K/sec                  
                 0      cpu-migrations            #    0,000 K/sec                  
           108 584      page-faults               #    0,294 M/sec                  
     1 175 946 161      cycles                    #    3,180 GHz                    
     2 086 554 968      instructions              #    1,77  insn per cycle         
       422 531 402      branches                  # 1142,636 M/sec                  
           768 338      branch-misses             #    0,18% of all branches        

       0,370103043 seconds time elapsed

与ctypes具有set相同数量的分支未命中的代码相比,具有更少页面错误的代码比其他两个更少.我唯一看到的是有更多的指令和分支(但我仍然不知道为什么)和更多的上下文切换(但它肯定是更长的运行时间而不是原因的结果).

因此,我有两个问题:

1. 为什么ctypes这么慢？
2. 有没有办法提高性能,无论是使用ctype还是使用其他库？

Answer 1

虽然这不是一个明确的答案,但问题似乎是构造函数调用*t.相反,执行以下操作可显着降低开销:

array =  (ctypes.c_uint32 * len(t))()
array[:] = t

测试:

import timeit
setup="from array import array; import ctypes; t = [i for i in range(1000000)];"
print(timeit.timeit(stmt='(ctypes.c_uint32 * len(t))(*t)',setup=setup,number=10))
print(timeit.timeit(stmt='a = (ctypes.c_uint32 * len(t))(); a[:] = t',setup=setup,number=10))
print(timeit.timeit(stmt='array("I",t)',setup=setup,number=10))
print(timeit.timeit(stmt='set(t)',setup=setup,number=10))

输出:

1.7090932869978133
0.3084979929990368
0.08278547400186653
0.2775516299989249

Answer 2

解决方法是使用array模块并强制转换地址或使用 from_buffer 方法...

import timeit
setup="from array import array; import ctypes; t = [i for i in range(1000000)];"
print(timeit.timeit(stmt="v = array('I',t);assert v.itemsize == 4; addr, count = v.buffer_info();p = ctypes.cast(addr,ctypes.POINTER(ctypes.c_uint32))",setup=setup,number=10))
print(timeit.timeit(stmt="v = array('I',t);a = (ctypes.c_uint32 * len(v)).from_buffer(v)",setup=setup,number=10))
print(timeit.timeit(stmt='(ctypes.c_uint32 * len(t))(*t)',setup=setup,number=10))
print(timeit.timeit(stmt='set(t)',setup=setup,number=10))

使用 Python 3 时速度要快很多倍：

$ python3 convert.py
0.08303386811167002
0.08139665238559246
1.5630637975409627
0.3013848252594471