Improve performance of dpnp.nanmedian

[vtavana]

In this PR, implementation of dpnp.nanmedian is update to improve performance.
There is a significant improvement when axis is not None as shown in tables below.

Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) Data Center GPU Max 1100 12.60.7 [1.6.31294+9]
Intel(R) OpenCL, Intel(R) Xeon(R) Platinum 8480+ OpenCL 3.0 (Build 0) [2024.18.9.0.28_160000]

import dpnp, numpy
a = numpy.ones((16*8192, 32))
axis = 1
# Randomly set some elements to NaN:
w = numpy.random.random((len(a.shape), 200)) * numpy.array(a.shape)[:, None]
w = w.astype(numpy.intp)
a[tuple(w)] = numpy.nan
%timeit numpy.nanmedian(a, axis=axis)

a_cpu = dpnp.array(a, device="cpu")
%timeit dpnp.nanmedian(a_cpu, axis=axis); a_cpu.sycl_queue.wait();

a_gpu = dpnp.array(a, device="gpu")
%timeit dpnp.nanmedian(a_gpu, axis=axis); a_gpu.sycl_queue.wait();

Old implementation:

size, axis	NumPy	CPU-Xeon	GPU-PVC
(2048, 2048), axis=None	19.3 ms ± 191 μs	27.1 ms ± 7.1 ms	4.38 ms ± 5.39 μs
(2048, 2048), axis=0	74.7 ms ± 66.7 μs	3.24 s ± 63.7 ms	1.23 s ± 5.14 ms
(2048, 2048), axis=1	34.1 ms ± 56.1 μs	3.21 s ± 58.1 ms	1.25 s ± 9.01 ms
(16*8192, 32), axis=None	18.7 ms ± 41.1 μs	25.5 ms ± 4.5 ms	4.41 ms ± 16.1
(16*8192, 32), axis=0	51.8 ms ± 83 μs	72.5 ms ± 4.56 ms	20.1 ms ± 215 μs
(16*8192, 32), axis=1	89.2 ms ± 265 μs	3min 31s ± 1.53 s	1min 19s ± 1.28 s

New implementation:

size, axis	NumPy	CPU-Xeon	GPU-PVC
(2048, 2048), axis=None	19.3 ms ± 67.7 μs	24.8 ms ± 3.61 ms	4.97 ms ± 15.6 μs
(2048, 2048), axis=0	74.2 ms ± 50.3 μs	29.2 ms ± 2.93 ms	3.99 ms ± 217 μs
(2048, 2048), axis=1	34.1 ms ± 64.3 μs	20.3 ms ± 4.25 ms	3.45 ms ± 81.7 μs
(16*8192, 32), axis=None	20 ms ± 221 μs	24.8 ms ± 4.34 ms	5.03 ms ± 12 μs
(16*8192, 32), axis=0	52.2 ms ± 77.6 μs	24.3 ms ± 1.97 ms	4.57 ms ± 194 μs
(16*8192, 32), axis=1	90.4 ms ± 305 μs	31.7 ms ± 8.73 ms	4.67 ms ± 78 μs

• 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?

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/2240/index.html

[coveralls]

coverage: 65.074% (-0.008%) from 65.082%
when pulling a2ab7ca on update-nanmedian
into ea718e3 on master.

[antonwolfy]

This brings great performance improvements. Thank you @vtavana, LGTM