Performance improvements

• Accelerate axis-wise reductions on GPUs by combining multiple kernel invocations into one.
• Parallelize specialized implementation for cupynumeric.take, and use it in more cases, including cupynumeric.take_along_axis.

UX improvements

• I/O functions (e.g. hdf5 to_file) and memory offloading (e.g. offload_to) functions from Legate now accept cuPyNumeric ndarrays directly.

Known issues

• We are aware of hangs when using cuSolverMp-based APIs on 4+ Perlmutter nodes. This appears to be a cluster-specific issue, that we are investigating.
• We are aware of hangs when using UCX 1.19 with the CUDA 13 conda packages. These are typically accompanied by an error message like this:

  ib_md.c:287  UCX  ERROR ibv_reg_mr(address=(nil), length=134217728, access=0xf) failed: Bad address
  ucp_mm.c:76   UCX  ERROR failed to register address (nil) (cuda) length 134217728 on md[6]=mlx5_0: Input/output error (md supports: host|cuda)
  

  We are investigating a proper fix. For the time being, setting UCX_MEMTYPE_CACHE=no in the environment appears to resolve the hang, at the cost of potentially decreasing UCX performance.

Full Changelog: https://github.com/nv-legate/cupynumeric/compare/v25.10.00...v25.11.00