Bug: GCXS slicing can take inordinate amount of time / crash kernel

[dani-corie]

sparse version checks

• I checked that this issue has not been reported before list of issues.

• I have confirmed this bug exists on the latest version of sparse.

• I have confirmed this bug exists on the main branch of sparse.

Describe the bug

I have noticed that under some circumstances, slicing a GCXS array will take an unexpecteda amount of time, or crash the kernel after a runtime of >45 seconds.

Steps or code to reproduce the bug

a = sparse.COO(
  [[1, 100, 215, 66],[5, 101, 242, 11],[3, 5, 1, 11],[13, 1, 3, 1],[55, 1, 6, 8]],
  [5, 10, 2, 1],
  shape=(255, 255, 255, 255, 255)
)
b = a.asformat('gcxs')
b[1, :, :, :, :]

Expected results

A slice returned within a short amount of time, provided the sparsity of the matrix.

b[:, 1, :, :, :], b[:, :, 1, :, :] and b[:, :, :, 1, :] return after a fraction of a second.
b[: :, :, :, 1] for some reason takes ~4 seconds on my machine.

Actual results

Kernel crash after >45 seconds of runtime.

Please describe your system.

1. OS and version: Ubuntu 22.04, latest update
2. sparse version: '0.16.0b4'
3. NumPy version: '2.1.3'
4. Numba version: '0.61.0'

Relevant log output

The Kernel crashed while executing code in the current cell or a previous cell. 
Please review the code in the cell(s) to identify a possible cause of the failure. 
Click here for more info. 
View Jupyter log for further details.



19:39:49.903 [info] Restarted f3b136f3-350b-49cc-90d0-e337e991f066
19:46:21.391 [error] Disposing session as kernel process died ExitCode: undefined, Reason: 

(this is the full, unabridged log output from the crashed session)

[dani-corie]

Here's a transcript of running the same in a python repl:

$ python
>>> import sparse
>>> a = sparse.COO(
...   [[1, 100, 215, 66],[5, 101, 242, 11],[3, 5, 1, 11],[13, 1, 3, 1],[55, 1, 6, 8]],
...   [5, 10, 2, 1],
...   shape=(255, 255, 255, 255, 255)
... )
>>> b = a.asformat('gcxs')
>>> b[1, :, :, :, :]
Killed
$

$ python --version
Python 3.13.1