cuNumeric is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using cuNumeric you do things like run the final example of the Python CFD course completely unmodified on 2048 A100 GPUs in a DGX SuperPOD and achieve good weak scaling.

cuNumeric works best for programs that have very large arrays of data that cannot fit in the memory of a single GPU or a single node and need to span multiple nodes and GPUs. While our implementation of the current NumPy API is still incomplete, programs that use unimplemented features will still work (assuming enough memory) by falling back to the canonical NumPy implementation.

If you have questions, please contact us at legate(at)nvidia.com.

cuNumeric is available on conda. Create a new environment containing cuNumeric:

mamba create -n myenv -c nvidia -c conda-forge -c legate cunumeric

or install it into an existing environment:

mamba install -c nvidia -c conda-forge -c legate cunumeric

Only linux-64 packages are available at the moment.

The default package contains GPU support, and is compatible with CUDA >= 11.8 (CUDA driver version >= r520), and Volta or later GPU architectures. There are also CPU-only packages available, and will be automatically selected when installing on a machine without GPUs. You can force installation of a CPU-only package by requesting it as follows:

mamba ... cunumeric=*=*_cpu

See the build instructions at https://nv-legate.github.io/cunumeric for details about building cuNumeric from source.

Using cuNumeric as a replacement for NumPy is easy. Users only need to replace:

import numpy as np

with:

import cunumeric as np

These programs can then be run by the Legate driver script described in the Legate Core documentation.

legate cunumeric_program.py

For execution with multiple nodes (assuming Legate Core is installed with networking support) users can supply the --nodes option. For execution with GPUs, users can use the --gpus flags to specify the number of GPUs to use per node. We encourage all users to familiarize themselves with these resource flags as described in the Legate Core documentation or simply by passing --help to the legate driver script.

You can use test.py to run the test suite. Invoke the script directly or through standard python; the script will invoke the legate driver script internally. Check out test.py --help for further options.

cuNumeric is currently a work in progress and we are gradually adding support for additional NumPy operators. Unsupported NumPy operations will provide a warning that we are falling back to canonical NumPy. Please report unimplemented features that are necessary for attaining good performance so that we can triage them and prioritize implementation appropriately. The more users that report an unimplemented feature, the more we will prioritize it. Please include a pointer to your code if possible too so we can see how you are using the feature in context.

cuNumeric currently supports the following NumPy types: float16, float32, float64, int16, int32, int64, uint16, uint32, uint64, bool, complex64, and complex128.

cuNumeric supports up to 4D arrays by default, you can adjust this setting by installing legate.core with a larger --max-dim.

The cuNumeric documentation can be found here.

There are three primary directions that we plan to investigate with cuNumeric going forward:

• More features: we plan to identify a few key lighthouse applications and use the demands of these applications to drive the addition of new features to cuNumeric.
• We plan to add support for sharded file I/O for loading and storing large data sets that could never be loaded on a single node. Initially this will begin with native support for hdf5 and zarr, but will grow to accommodate other formats needed by our lighthouse applications.
• Strong scaling: while cuNumeric is currently implemented in a way that enables weak scaling of codes on larger data sets, we would also like to make it possible to strong-scale Legate applications for a single problem size. This will require leveraging some of the more advanced features of Legion from inside the Python interpreter.

We are open to comments, suggestions, and ideas.

See the discussion of contributing in CONTRIBUTING.md.

• When using certain operations with high scratch space requirements (e.g. einsum or convolve) you might run into the following error:

  LEGION ERROR: Failed to allocate DeferredBuffer/Value/Reduction in task [some task] because [some memory] is full. This is an eager allocation ...
  

  Currently, Legion splits its memory reservations between two pools: the "deferred" pool, used for allocating cuNumeric ndarrays, and the "eager" pool, used for allocating scratch memory for operations. The above error message signifies that not enough memory was available for an operation's scratch space requirements. You can work around this by allocating more memory overall to cuNumeric (e.g. adjusting --sysmem, --numamem or --fbmem), and/or by adjusting the split between the two pools (e.g. by passing -lg:eager_alloc_percentage 60 on the command line to allocate 60% of memory to the eager pool, up from the default of 50%).
• cuNumeric can exercise a bug in OpenBLAS when it is run with multiple OpenMP processors
• On Mac OSX, cuNumeric can trigger a bug in Apple's implementation of libc++. The bug has since been fixed but likely will not show up on most Apple machines for quite some time. You may have to manually patch your implementation of libc++. If you have trouble doing this please contact us and we will be able to help you.