Performance

The main aim of lazyarray is to improve performance (increased speed and reduced memory use) in two scenarios:

  • arrays are used conditionally (i.e. there are cases in which the array is never used);
  • only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array).

However, at the same time use of larray objects should not be too much slower than plain NumPy arrays in normal use.

Here we see that using a lazyarray adds minimal overhead compared to using a plain array:

>>> from timeit import repeat
>>> repeat('np.fromfunction(lambda i,j: i*i + 2*i*j + 3, (5000, 5000))',
...        setup='import numpy as np', number=1, repeat=5)
[1.9397640228271484, 1.92628812789917, 1.8796701431274414, 1.6766629219055176, 1.6844701766967773]
>>> repeat('larray(lambda i,j: i*i + 2*i*j + 3, (5000, 5000)).evaluate()',
...        setup='from lazyarray import larray', number=1, repeat=5)
[1.686661958694458, 1.6836578845977783, 1.6853220462799072, 1.6538069248199463, 1.645576000213623]

while if we only need to evaluate part of the array (perhaps because the other parts are being evaluated on other nodes), there is a major gain from using a lazy array.

>>> repeat('np.fromfunction(lambda i,j: i*i + 2*i*j + 3, (5000, 5000))[:, 0:4999:10]',
...        setup='import numpy as np', number=1, repeat=5)
[1.691796064376831, 1.668884038925171, 1.647057056427002, 1.6792259216308594, 1.652547836303711]
>>> repeat('larray(lambda i,j: i*i + 2*i*j + 3, (5000, 5000))[:, 0:4999:10]',
...        setup='from lazyarray import larray', number=1, repeat=5)
[0.23157119750976562, 0.16121792793273926, 0.1594078540802002, 0.16096210479736328, 0.16096997261047363]

Note

These timings were done on a MacBook Pro 2.8 GHz Intel Core 2 Duo with 4 GB RAM, Python 2.7 and NumPy 1.6.1.

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