Parallelize convolve_2d numpy kernel (#3615)

[brendancol]

Fixes #3615.

Problem

_convolve_2d_numpy iterates its two outer loops with numba.prange but was decorated @jit(nopython=True, nogil=True) with no parallel=True. Numba only parallelizes prange when parallel=True is set, so as shipped the loop ran serial on a single core. This is the main CPU convolution path, used by the numpy backend and per chunk by the dask+numpy backend.

Fix

• Add parallel=True to the decorator.
• Serialize the kernel launch behind a module-level threading.Lock. Dask calls the kernel per chunk under its threaded scheduler, and numba's default workqueue threading layer is not threadsafe across host threads (SIGABRT on macOS). This is the same hazard and fix already applied to terrain.py (Streaming reproject thread pool aborts the process when numba parallel kernels run concurrently #3141). A single numpy call takes the lock uncontended and still runs across all cores; concurrent dask chunk calls run one at a time, each internally parallel.

The cupy and dask+cupy paths use a separate CUDA kernel and are untouched.

Measurement

20-core host, 2000x2000 float64 raster, 15x15 kernel, end-to-end through convolve_2d:

• before: ~356 ms
• after: ~53 ms

Results are identical (verified np.allclose(..., equal_nan=True)).

Verification

• xrspatial/tests/test_convolution.py: 6 passed.
• numpy vs dask+numpy parity across all four boundary modes (nan, nearest, reflect, wrap): match.
• cupy vs numpy parity: match.
• Concurrent dask threaded scheduler (8 workers): runs clean, no crash or deadlock.

Benchmark

Adds benchmarks/benchmarks/convolution.py with Convolve2d (numpy/cupy/dask across sizes and kernel sizes) and CircleKernel, so the serial regression cannot silently return.

Performance context

• OOM verdict: SAFE. Peak dask memory scales with chunk size plus overlap depth, not the full array (~20 tasks per chunk).
• Bottleneck: compute-bound.
• Affected backends: numpy (direct speedup), dask+numpy (per-chunk speedup, serialized behind the lock).