# `cuda.core` v0.1.1 Release notes

Released on Dec 20, 2024

## Hightlights

- Add `StridedMemoryView` and `@args_viewable_as_strided_memory` that provide a concrete
  implementation of DLPack & CUDA Array Interface supports.
- Add `Linker` that can link one or multiple `ObjectCode` instances generated by `Program`. Under
  the hood, it uses either the nvJitLink or driver (`cuLink*`) APIs depending on the CUDA version
  detected in the current environment.
- Support `pip install cuda-core`. Please see the Installation Guide for further details.

## New features

- Add a `cuda.core.experimental.system` module for querying system- or process- wide information.
- Add `LaunchConfig.cluster` to support thread block clusters on Hopper GPUs.

## Enchancements

- The internal handle held by `ObjectCode` is now lazily initialized upon first touch.
- Support TCC devices with a default synchronous memory resource to avoid the use of memory pools.
- Ensure `"ltoir"` is a valid code type to `ObjectCode`.
- Document the `__cuda_stream__` protocol.
- Improve test coverage & documentation cross-references.
- Enforce code formatting.

## Bug fixes

- Eliminate potential class destruction issues.
- Fix circular import during handling a foreign CUDA stream.

## Limitations

- All APIs are currently *experimental* and subject to change without deprecation notice.
  Please kindly share your feedbacks with us so that we can make `cuda.core` better!
- Using `cuda.core` with NVRTC or nvJitLink installed from PyPI via `pip install` is currently
  not supported. This will be fixed in a future release.
- Some `LinkerOptions` are only available when using a modern version of CUDA. When using CUDA <12,
  the backend is the cuLink api which supports only a subset of the options that nvjitlink does.
  Further, some options aren't available on CUDA versions <12.6.
- To use `cuda.core` with Python 3.13, it currently requires building `cuda-python` from source
  prior to `pip install`. This extra step will be fixed soon.