[Feature Request] Poor performance of ppm/dplr for long-range interactions

### Summary

`kspace_style pppm/dplr` is quite slow in LAMMPS, significantly slowing down the MD simulation. A multiprocessing code running on CPU (or a GPU implementation) could speed-up significantly the simulation.

### Detailed Description

Hi, I'm running a `dplr` MD simulations with LAMMPS and I am facing low performances in the long-range part of the calculation. I'm running on 4 GPUs in a single node using 1 MPI process for each gpu. This is the performance report I get at the end of my simulation of 2727 atoms (and 909 wannier centroids):

```
Performance: 0.156 ns/day, 153.955 hours/ns, 9.021 timesteps/s, 32.802 katom-step/s
100.7% CPU use with 4 MPI tasks x no OpenMP threads

MPI task timing breakdown:
Section | min time | avg time | max time |%varavg| %total
---------------------------------------------------------------
Pair | 13.74 | 14.367 | 15.688 | 20.6 | 4.89
Bond | 0.0011576 | 0.0013793 | 0.0016772 | 0.5 | 0.00
Kspace | 157.22 | 164.76 | 171.03 | 46.2 | 56.09
Neigh | 2.6626 | 2.6628 | 2.6631 | 0.0 | 0.91
Comm | 0.30548 | 0.34742 | 0.43454 | 8.8 | 0.12
Output | 0.0019177 | 0.0024892 | 0.0027186 | 0.7 | 0.00
Modify | 105.74 | 111.57 | 117.8 | 50.2 | 37.98
Other | | 0.03181 | | | 0.01

```
It seems that the` kspace_style pppm/dplr`, which is used to account for the long-range interactions, is quite slow in LAMMPS, significantly slowing down the MD simulation. Using more GPUS does not increase significantly the performance since it improve only the `Pair` time.

Do you think it would be beneficial to implement OpenMP thread parallelization to speed this part up? Perhaps using GPUs for both the short-range NNP and the Wannier NN, while using **multiple processes** on multiple CPUs for the particle-particle particle-mesh solver? Could a GPU `pppm/dplr` code also increase the performance?

### Further Information, Files, and Links

_No response_