我同意@aix，multiprocessing绝对是要走的路。无论您将如何进行I / O绑定，无论您正在运行多少个并行进程，您都只能读得这么快。但是，很容易被 一些 加速。
考虑以下内容（input /是一个包含来自Gutenberg项目的.txt文件的目录）。
import os.path
from multiprocessing import Pool
import sys
import time
def process_file(name):
''' Process one file: count number of lines and words '''
linecount=0
wordcount=0
with open(name, 'r') as inp:
for line in inp:
linecount+=1
wordcount+=len(line.split(' '))
return name, linecount, wordcount
def process_files_parallel(arg, dirname, names):
''' Process each file in parallel via Poll.map() '''
pool=Pool()
results=pool.map(process_file, [os.path.join(dirname, name) for name in names])
def process_files(arg, dirname, names):
''' Process each file in via map() '''
results=map(process_file, [os.path.join(dirname, name) for name in names])
if __name__ == '__main__':
start=time.time()
os.path.walk('input/', process_files, None)
print "process_files()", time.time()-start
start=time.time()
os.path.walk('input/', process_files_parallel, None)
print "process_files_parallel()", time.time()-start
当我在双核计算机上运行此程序时，速度明显提高（但不是2倍）：
$ python process_files.py
process_files() 1.71218085289
process_files_parallel() 1.28905105591
如果文件足够小以适合内存，并且您需要完成很多不受I / O约束的处理，那么您应该会看到更好的改进。
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