fix: use parallelized numba functions if possible

[ilan-gold]

This is basically a one-to-one port of the scanpy function. It might make sense to export this function from this package but maybe it's a weird fit as well. Not sure!

[codecov]

Codecov Report

❌ Patch coverage is 26.66667% with 33 lines in your changes missing coverage. Please review.
✅ Project coverage is 42.82%. Comparing base (395b37e) to head (b073c49).

Files with missing lines	Patch %	Lines
src/fast_array_utils/utils.py	28.57%	30 Missing ⚠️
src/fast_array_utils/conv/scipy/_to_dense.py	0.00%	3 Missing ⚠️

❗ There is a different number of reports uploaded between BASE (395b37e) and HEAD (b073c49). Click for more details.

HEAD has 4 uploads less than BASE

Flag	BASE (395b37e)	HEAD (b073c49)
	6	2

Additional details and impacted files

@@             Coverage Diff             @@
##             main     #155       +/-   ##
===========================================
- Coverage   96.78%   42.82%   -53.96%     
===========================================
  Files          19       16        -3     
  Lines         466      418       -48     
===========================================
- Hits          451      179      -272     
- Misses         15      239      +224     

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[codspeed-hq]

Merging this PR will degrade performance by 33.61%

❌ 6 regressed benchmarks
✅ 226 untouched benchmarks

⚠️ Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Benchmark	BASE	HEAD	Efficiency
❌	test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-float32-is_constant]	1.7 ms	2.4 ms	-30.45%
❌	test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-int32-is_constant]	1.7 ms	2.4 ms	-29.55%
❌	test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-float64-is_constant]	1.7 ms	2.6 ms	-33.61%
❌	test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float64-is_constant]	1.7 ms	2.4 ms	-30.11%
❌	test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float32-is_constant]	1.7 ms	2.4 ms	-28.38%
❌	test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-int32-is_constant]	1.7 ms	2.4 ms	-29.91%

---

_{Comparing ig/parallel_kernels (54584b2) with main (395b37e)}

[ilan-gold]

The codspeed looks to be entirely overhead, which I take to be on account of the size of the data. Otherwise, the actual function calls are faster. WDYT @flying-sheep ?

[flying-sheep]

why is that? isn’t our decorator supposed to prevent issues?

[flying-sheep]

Can you explain dask_single_threaded? isn’t that a red flag? Shouldn’t numba just work in dask with our decorator?

[flying-sheep]

OK, running with the right flags shows the warning how users would see it:

❯ hatch test -- -s -p no:warnings tests/test_stats.py::test_is_constant
[…]                                                                                                                                                                                                                                                                                                                      
tests/test_stats.py ...sss...............sss./Users/philipp.angerer/Dev/Python/fast-array-utils/src/fast_array_utils/stats/_is_constant.py:66: UserWarning: Detected unsupported threading environment. Trying to run _is_constant_cs_major in serial mode. In case of problems, install `tbb`.
  return _is_constant_cs_major(a, shape)
Numba workqueue threading layer is terminating: Concurrent access has been detected.

 - The workqueue threading layer is not threadsafe and may not be accessed concurrently by multiple threads. Concurrent access typically occurs through a nested parallel region launch or by calling Numba parallel=True functions from multiple Python threads.
 - Try using the TBB threading layer as an alternative, as it is, itself, threadsafe. Docs: https://numba.readthedocs.io/en/stable/user/threading-layer.html

Fatal Python error: Aborted
[…]

so numba actually has something more to say and has the same recommendation