parallel dask for loop slower than regular loop?

Question

If I try to parallelize a for loop with dask, it ends up executing slower than the regular version. Basically, I just follow the introductory example from the dask tutorial, but for some reason it's failing on my end. What am I doing wrong?

In [1]: import numpy as np
   ...: from dask import delayed, compute
   ...: import dask.multiprocessing

In [2]: a10e4 = np.random.rand(10000, 11).astype(np.float16)
   ...: b10e4 = np.random.rand(10000, 11).astype(np.float16)

In [3]: def subtract(a, b):
   ...:     return a - b

In [4]: %%timeit
   ...: results = [subtract(a10e4, b10e4[index]) for index in range(len(b10e4))]
1 loop, best of 3: 10.6 s per loop

In [5]: %%timeit
   ...: values = [delayed(subtract)(a10e4, b10e4[index]) for index in range(len(b10e4)) ]
   ...: resultsDask = compute(*values, get=dask.multiprocessing.get)
1 loop, best of 3: 14.4 s per loop

Answer 1

Two issues:

1. Dask introduces about a millisecond of overhead per task. You'll want to ensure that your computations take significantly longer than that.
2. When using the multiprocessing scheduler data gets serialized between processes, which can be quite expensive. See http://dask.pydata.org/en/latest/setup.html