Improve performance of dnp.nan_to_num

[ndgrigorian]

This PR adds a dedicated kernel for dnp.nan_to_num to improve its performance. This reduces the number of kernel calls to at most one in all cases.

A kernel for both strided and contiguous inputs have been added, to avoid additional allocation of device memory for trivial strides when input is fully C- or F-contiguous.

For example of performance gains, using Max GPU

master:

In [1]: import dpnp as dnp

In [2]: import numpy as np

In [3]: x_np = np.random.randn(10**9)

In [4]: x_np[np.random.choice(x_np.size, 200, replace=False)] = np.nan

In [5]: x = dnp.asarray(x_np)

In [6]: q = x.sycl_queue

In [7]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 394 ms, sys: 43.8 ms, total: 438 ms
Wall time: 304 ms

In [8]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 333 ms, sys: 31.8 ms, total: 364 ms
Wall time: 134 ms

on branch:

In [8]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 49.6 ms, sys: 8.1 ms, total: 57.7 ms
Wall time: 60.9 ms

In [9]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 22.9 ms, sys: 16 ms, total: 38.9 ms
Wall time: 19.7 ms

• Have you provided a meaningful PR description?
• Have you added a test, reproducer or referred to issue with a reproducer?
• Have you tested your changes locally for CPU and GPU devices?
• Have you made sure that new changes do not introduce compiler warnings?
• Have you checked performance impact of proposed changes?
• If this PR is a work in progress, are you filing the PR as a draft?

[coveralls]

coverage: 71.298% (-0.02%) from 71.316%
when pulling 54dfaf5 on ndgrigorian:improve-nan-to-num-performance
into 91161a8 on IntelPython:master.