dask.array performance compared to NumPy arrays in color conversion

dask_benchmarks.py

import numpy as np
import dask.array as da

# Benchmark: NumPy with array construction included
%timeit nparr0 = np.random.random(size=(10000, 1000)); \
        nparr1 = np.random.random(size=(10000, 1000)); \
        np.sqrt((nparr0 - nparr1) ** 2 + (nparr0 - nparr1) ** 2 + (nparr0 - nparr1) ** 2)

# Benchmark: Dask with dask.array construction included
%timeit x = da.random.random(size=(10000, 1000), chunks=(10000, 1000)); \
        y = da.random.random(size=(10000, 1000), chunks=(10000, 1000)); \
        (da.sqrt((x - y) ** 2 + (x - y) ** 2 + (x - y) ** 2)).compute()

# Benchmark (unfair): compute using existing NumPy arrays
nparr0 = np.random.random(size=(10000, 1000))
nparr1 = np.random.random(size=(10000, 1000))
%timeit np.sqrt((nparr0 - nparr1) ** 2 + (nparr0 - nparr1) ** 2 + (nparr0 - nparr1) ** 2)

# Benchmark: dask.from_array() from NumPy arrays
%timeit x = da.from_array(nparr0, chunks=(10000, 1000)); \
        y = da.from_array(nparr1, chunks=(10000, 1000)); \
        (da.sqrt((x - y) ** 2 + (x - y) ** 2 + (x - y) ** 2)).compute()