Introduction

Numexpr 2.0 leverages a new virtual machine completely based on the new ndarray iterator introduced in NumPy 1.6. This represents a nice combination of the advantages of using the new iterator, while retaining the ability to avoid copies in memory as well as the multi-threading capabilities of the previous virtual machine (1.x series).

The increased performance of the new virtual machine can be seen in several scenarios, like:

• Broadcasting. Expressions containing arrays that needs to be broadcasted, will not need additional memory (i.e. they will be broadcasted on-the-fly).

• Non-native dtypes. These will be translated to native dtypes on-the-fly, so there is not need to convert the whole arrays first.

• Fortran-ordered arrays. The new iterator will find the best path to optimize operations on such arrays, without the need to transpose them first.

There is a drawback though: performance with small arrays suffers a bit because of higher set-up times for the new virtual machine. See below for detailed benchmarks.

Some benchmarks for best-case scenarios

Here you have some benchmarks of some scenarios where the new virtual machine actually represents an advantage in terms of speed (also memory, but this is not shown here). As you will see, the improvement is notable in many areas, ranging from 3x to 6x faster operations.

Broadcasting

>>> a = np.arange(1e3)
>>> b = np.arange(1e6).reshape(1e3, 1e3)

>>> timeit ne.evaluate("a*(b+1)")   # 1.4.2
100 loops, best of 3: 16.4 ms per loop

>>> timeit ne.evaluate("a*(b+1)")  # 2.0
100 loops, best of 3: 5.2 ms per loop

Non-native types

>>> a = np.arange(1e6, dtype=">f8")
>>> b = np.arange(1e6, dtype=">f8")

>>> timeit ne.evaluate("a*(b+1)")  # 1.4.2
100 loops, best of 3: 17.2 ms per loop

>>> timeit ne.evaluate("a*(b+1)")  # 2.0
100 loops, best of 3: 6.32 ms per loop

Fortran-ordered arrays

>>> a = np.arange(1e6).reshape(1e3, 1e3).copy('F')
>>> b = np.arange(1e6).reshape(1e3, 1e3).copy('F')

>>> timeit ne.evaluate("a*(b+1)")  # 1.4.2
10 loops, best of 3: 32.8 ms per loop

>>> timeit ne.evaluate("a*(b+1)")  # 2.0
100 loops, best of 3: 5.62 ms per loop

Mix of 'non-native' arrays, Fortran-ordered, and using broadcasting

>>> a = np.arange(1e3, dtype='>f8').copy('F')
>>> b = np.arange(1e6, dtype='>f8').reshape(1e3, 1e3).copy('F')

>>> timeit ne.evaluate("a*(b+1)")  # 1.4.2
10 loops, best of 3: 21.2 ms per loop

>>> timeit ne.evaluate("a*(b+1)")  # 2.0
100 loops, best of 3: 5.22 ms per loop

Larger setup-time

The only drawback of the new virtual machine is during the computation of small arrays:

>>> a = np.arange(10)
>>> b = np.arange(10)

>>> timeit ne.evaluate("a*(b+1)")  # 1.4.2
10000 loops, best of 3: 22.1 µs per loop

>>> timeit ne.evaluate("a*(b+1)")  # 2.0
10000 loops, best of 3: 30.6 µs per loop

i.e. the new virtual machine takes a bit more time to set-up (around 8 µs in this machine). However, this should be not too important because for such a small arrays NumPy is always a better option:

>>> timeit c = a*(b+1)
100000 loops, best of 3: 4.16 µs per loop

And for arrays large enough the difference is negligible:

>>> a = np.arange(1e6)
>>> b = np.arange(1e6)

>>> timeit ne.evaluate("a*(b+1)")  # 1.4.2
100 loops, best of 3: 5.77 ms per loop

>>> timeit ne.evaluate("a*(b+1)")  # 2.0
100 loops, best of 3: 5.77 ms per loop

Conclusion

The new virtual machine introduced in numexpr 2.0 brings more performance in many different scenarios (broadcast, non-native dtypes, Fortran-orderd arrays), while it shows slightly worse performance for small arrays. However, as numexpr is more geared to compute large arrays, the new virtual machine should be good news for numexpr users in general.