I need to load (de-serialize) a pre-computed list of integers from a file in a Python script (into a Python list). The list is large (upto millions of items), and I can choose the format I store it in, as long as loading is fastest.
Which is the fastest method, and why?

Using import on a .py file that just contains the list assigned to a variable
Using cPickle's load
Some other method (perhaps numpy?)

Also, how can one benchmark such things reliably?
Addendum: measuring this reliably is difficult, because import is cached so it can't be executed multiple times in a test. The loading with pickle also gets faster after the first time probably because page-precaching by the OS. Loading 1 million numbers with cPickle takes 1.1 sec the first time run, and 0.2 sec on subsequent executions of the script.
Intuitively I feel cPickle should be faster, but I'd appreciate numbers (this is quite a challenge to measure, I think). 
And yes, it's important for me that this performs quickly.
Thanks
cPickle will be the fastest since it is saved in binary and no real python code has to be parsed.
Other advantates are that it is more secure (since it does not execute commands) and you have no problems with setting $PYTHONPATH correctly.
Python list serialization - fastest method