# Profiling

## Total runtime measurements

Ipython and the jupyter notebook offer two useful commands to measure the time a single line or cell of code takes to execute:

• %time will time the total runtime in a simple way (much like the command time in a UNIX shell) – if your command/script takes a very long time to run, this is what you want to use.
• %timeit will repeat the time measurements many times: by default it will do 3 trials, where each trial will execute the command N times. The number N is chosen so that the total test run takes a couple of seconds and the reported time will be the one of the best trial. This gives much more precise measurements for short-running commands.
In [2]: square_ar = numpy.random.rand(1000, 1000)
In [3]: %time w, v =  numpy.linalg.eig(square_ar)
CPU times: user 4.54 s, sys: 240 ms, total: 4.78 s
Wall time: 2.44 s

For small computations that are repeated many times, timeit is the better tool:

In [4]: %timeit square_ar.var()
The slowest run took 5.01 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 6.05 ms per loop

We get a warning message, most likely because the very first run was much slower than the other runs due to cache effects (data that was previously used is in a fast memory and can be reused very efficiently). Nowadays a lot of performance optimization revolves around the efficient use of memory in general and caches in particular. Whether we are interested in the results including these effects or not depends on our question, but if we are only interested in the “pure computation” time then one strategy is to scale up the problem size:

In [5]: square_ar = numpy.random.rand(3000, 3000)
In [6]: %timeit square_ar.var()
10 loops, best of 3: 101 ms per loop

For the timing of a series of statements, %%timeit can be used in the first line of a jupyter notebook cell to time the full cell.

## sum vs. sum

numpy has a sum function, but sum is also a standard built-in function in Python. Both can be used with all kind of Python sequences, e.g. with Python lists or numpy arrays. Use a = numpy.arange(1000000) and l = list(range(1000000)) as example data and compare the runtime of sum vs. numpy.sum for the two variables. Which function is faster. Can you guess why?