On Sun, 05 Feb 2006 19:14:29 +1100, Steven D'Aprano <[EMAIL PROTECTED]> wrote:
>On Sun, 05 Feb 2006 03:31:24 +0000, Neil Schemenauer wrote: > >> Peter Hansen <[EMAIL PROTECTED]> wrote: >>> More precisely, the state of the function is *saved* when a yield >>> occurs, so you certainly don't *recreate* it from scratch, but merely >>> restore the state, and this should definitely be faster than creating it >>> from scratch in the first place. >> >> Right. Resuming a generator is faster than calling a function. > >Have you actually measured this, or are you just making a wild guess? > >According to a short test performed by Magnus Lycka, resuming a generator >takes more time than calling a function. My own test agrees. > >Here is my test, using Python 2.3. I've tried to make the test as fair as >possible, with the same number of name lookups in both pieces of test code. > ># straight function, two name lookups > >>>> import timeit >>>> >>>> t1 = timeit.Timer(stmt="func.next()", setup= >... """class K: >... pass >... >... def next(): >... return 1 >... >... func = K() >... func.next = next >... """) >>>> >>>> t1.timeit() >0.63980388641357422 > > ># generator, two name lookups > >>>> t2 = timeit.Timer(stmt="gen.next()", setup= >... """def g(): >... while 1: yield 1 >... >... gen = g() >... """) >>>> >>>> t2.timeit() >0.82081794738769531 > > ># straight function, one name lookup > >>>> t3 = timeit.Timer(stmt="f()", setup= >... """def f(): >... return 1 >... """) >>>> >>>> t3.timeit() >0.47273492813110352 > > ># generator, one name lookup > >>>> t4 = timeit.Timer(stmt="gnext()", setup= >... """def g(): >... while 1: yield 1 >... >... gnext = g().next >... """) >>>> >>>> t4.timeit() >0.55085492134094238 > > >So on the basis of my tests, there is a small, but significant speed >advantage to _calling_ a function versus _resuming_ a generator. > >Of course the other advantages of generators often far outweigh the tiny >setup cost each time you call one. In addition, for any complex function >with significant execution time, the call/resume time may be an >insignificant fraction of the total execution time. There is little or no >point in avoiding generators due to a misplaced and foolish attempt to >optimise your code. > I show an advantage favoring generator resumption vs function call: >>> from time import clock >>> def f(): return clock() ... >>> def g(): yield clock(); yield clock() ... >>> max(f()-f() for x in xrange(10000)) -9.2190462142316409e-006 >>> max(f()-f() for x in xrange(10000)) -9.2190462139818408e-006 >>> max(float.__sub__(*g()) for x in xrange(10000)) -7.5428559682677587e-006 >>> max(float.__sub__(*g()) for x in xrange(10000)) -7.5428559682677587e-006 >>> max(float.__sub__(*g()) for x in xrange(10000)) -7.5428559682677587e-006 (It'll probably go ten times faster on a recent box ;-) Regards, Bengt Richter -- http://mail.python.org/mailman/listinfo/python-list
