v25.03.00

This is a beta release of cuPyNumeric.

Linux x86 and ARM conda packages are available for this release at https://anaconda.org/legate/cupynumeric.

Documentation for this release can be found at https://docs.nvidia.com/cupynumeric/25.03/.

New features

Licensing

• With this release the Legate framework, on which cuPyNumeric is based, becomes open-source, under the Apache-2.0 license. This makes the entire cuPyNumeric stack (anything above the CUDA library level) open-source.

Added functionality

• Matrix exponential: cupynumeric.linalg.expm
• Batched eigendecomposition: cupynumeric.linalg.eigvals & cupynumeric.linalg.eig

Performance improvements

• No longer doing unnecessary streaming when running matrix multiplication on a single processor/GPU.

UX improvements

• Add thelegate.core.ProfileRange Python context manager, to annotate sub-spans within a larger task span on the profiler visualization.
• Add the local_task_array helper function, that can be used in Python tasks to create a view over a Store/Array argument, using a NumPy or CuPy array as appropriate based on the type of memory where the data is located.

Documentation improvements

• Add a user guide chapter on accelerating multi-GPU HDF5 workloads.

Known issues

• We are aware of possible performance regressions when using UCX 1.18. We are temporarily restricting our packages to UCX <= 1.17 while we investigate this.