def main(): for i in xrange(10**8): pass main()
This piece of code in Python runs in (Note: The timing is done with the time function in BASH in Linux.)
real 0m1.841s user 0m1.828s sys 0m0.012s
However, if the for loop isn"t placed within a function,
for i in xrange(10**8): pass
then it runs for a much longer time:
real 0m4.543s user 0m4.524s sys 0m0.012s
Why is this?
Inside a function, the bytecode is:
2 0 SETUP_LOOP 20 (to 23) 3 LOAD_GLOBAL 0 (xrange) 6 LOAD_CONST 3 (100000000) 9 CALL_FUNCTION 1 12 GET_ITER >> 13 FOR_ITER 6 (to 22) 16 STORE_FAST 0 (i) 3 19 JUMP_ABSOLUTE 13 >> 22 POP_BLOCK >> 23 LOAD_CONST 0 (None) 26 RETURN_VALUE
At the top level, the bytecode is:
1 0 SETUP_LOOP 20 (to 23) 3 LOAD_NAME 0 (xrange) 6 LOAD_CONST 3 (100000000) 9 CALL_FUNCTION 1 12 GET_ITER >> 13 FOR_ITER 6 (to 22) 16 STORE_NAME 1 (i) 2 19 JUMP_ABSOLUTE 13 >> 22 POP_BLOCK >> 23 LOAD_CONST 2 (None) 26 RETURN_VALUE
You might ask why it is faster to store local variables than globals. This is a CPython implementation detail.
Remember that CPython is compiled to bytecode, which the interpreter runs. When a function is compiled, the local variables are stored in a fixed-size array (not a
dict) and variable names are assigned to indexes. This is possible because you can"t dynamically add local variables to a function. Then retrieving a local variable is literally a pointer lookup into the list and a refcount increase on the
PyObject which is trivial.
Contrast this to a global lookup (
LOAD_GLOBAL), which is a true
dict search involving a hash and so on. Incidentally, this is why you need to specify
global i if you want it to be global: if you ever assign to a variable inside a scope, the compiler will issue
STORE_FASTs for its access unless you tell it not to.
By the way, global lookups are still pretty optimised. Attribute lookups
foo.bar are the really slow ones!
Here is small illustration on local variable efficiency.