Changed inclusive sum implementation from recursive to iterative

Changed hyperparameter choices to be different for CPU and GPU, resulting
in 20% performance gain on GPU.

The non-recursive implementation allows to avoid repeated USM allocations,
resulting in performance gains for large arrays.

Furthermore, corrected base step kernel to accumulate in outputT rather than
in size_t, which additionally realizes savings when int32 is used as
accumulator type.

Using example from gh-1249, previously, on my Iris Xe laptop:

```
In [1]: import dpctl.tensor as dpt
   ...: ag = dpt.ones((8192, 8192), device='gpu', dtype='f4')
   ...: bg = dpt.ones((8192, 8192), device='gpu', dtype=bool)

In [2]: cg = ag[bg]

In [3]: dpt.all(cg == dpt.reshape(ag, -1))
Out[3]: usm_ndarray(True)

In [4]: %timeit -n 10 -r 3 cg = ag[bg]
212 ms ± 56 ms per loop (mean ± std. dev. of 3 runs, 10 loops each)
```

while with this change:

```
In [4]: %timeit -n 10 -r 3 cg = ag[bg]
178 ms ± 24.2 ms per loop (mean ± std. dev. of 3 runs, 10 loops each)
```