Core Python Programming (2nd Edition)

16 def sum(x):
17 sleep(0.1)
18 if x < 2: return 1
19 return (x + sum(x-1))
20
21 funcs = [fib, fac, sum]
22 n = 12
23
24 def main():
25 nfuncs = range(len(funcs))
26
27 print '*** SINGLE THREAD'
28 for i in nfuncs:
29 print 'starting', funcs[i].__name__, 'at:', \
30 ctime()
31 print funcs[i](n)
32 print funcs[i].__name__, 'finished at:', \
33 ctime()
34
35 print '\n*** MULTIPLE THREADS'
36 threads = []
37 for i in nfuncs:
38 t = MyThread(funcs[i], (n,),
39 funcs[i].__name__)
40 threads.append(t)
41
42 for i in nfuncs:
43 threads[i].start()
44
45 for i in nfuncs:
46 threads[i].join()
47 print threads[i].getResult()
48
49 print 'all DONE'
50
51 if __name__ == '__main__':
52 main()
Running in single-threaded mode simply involves calling the functions one at a time and displaying the
corresponding results right after the function call.
When running in multithreaded mode, we do not display the result right away. Because we want to keep
our MyThread class as general as possible (being able to execute callables that do and do not produce
output), we wait until the end to call the geTResult() method to finally show you the return values of
each function call.
Because these functions execute so quickly (well, maybe except for the Fibonacci function), you will
notice that we had to add calls to sleep() to each function to slow things down so that we can see how
threading may improve performance, if indeed the actual work had varying execution times you
certainly wouldn't pad your work with calls to sleep(). Anyway, here is the output:
$ mtfacfib.py
*** SINGLE THREAD
starting fib at: Sun Jun 18 19:52:20 2006