[Python] Why (x / y)[i] faster than x[i] / y[i]?

I'm new to CuPy and CUDA/GPU computing. Can someone explain why (x / y) faster than x / y?

When taking advantage of GPU accelerated computations, are there any guidelines that would allow me to quickly determine which operation is faster? Which to avoid benchmarking every operation.

# In VSCode Jupyter Notebook

import cupy as cp
from cupyx.profiler import benchmark

x = cp.arange(1_000_000)
y = (cp.arange(1_000_000) + 1) / 2
i = cp.random.randint(2, size=1_000_000) == 0
x, y, i

# Output:
(array([ 0, 1, 2, ..., 999997, 999998, 999999], shape=(1000000,), dtype=int32),
array([5.000000e-01, 1.000000e+00, 1.500000e+00, ..., 4.999990e+05,
4.999995e+05, 5.000000e+05], shape=(1000000,), dtype=float64),
array([ True, False, True, ..., True, False, True], shape=(1000000,), dtype=bool))


def test1(x, y, i):
return (x / y)

def test2(x, y, i):
return x / y

print(benchmark(test1, (x, y, i)))
print(benchmark(test2, (x, y, i)))

# Output:
test1: CPU: 175.164 us +/- 61.250 (min: 125.200 / max: 765.100) us GPU-0: 186.001 us +/- 67.314 (min: 134.144 / max: 837.568) us
test2: CPU: 342.364 us +/- 130.840 (min: 223.000 / max: 1277.600) us GPU-0: 368.133 us +/- 136.911 (min: 225.504 / max: 1297.408) us

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