# Numba Meeting: 2022-08-09

Attendees: brandon willard, Graham Markall, Kaustubh Chaudhari, LI Da, Luk, Shannon Quinn, Todd A. Anderson, stuart, Siu Kwan Lam, Val, Jim Pivarski

NOTE: All communication is subject to the [Numba Code of Conduct](https://github.com/numba/numba-governance/blob/accepted/code-of-conduct.md).

Please refer to [this calendar](https://calendar.google.com/calendar/embed?src=5rqnddm4gjrdfjm31fsv182epk%40group.calendar.google.com) for the next meeting date.

## 0. Discussion

- Dropping support for old Python versions:
- https://github.com/numba/numba/pull/8318
- https://github.com/numba/numba/pull/8319
- https://github.com/numba/numba/pull/7920
- Question: what is the minimum supported Python for 0.57 and when can/should we merge the patches to remove support?
- What implications does this have for CI??
- Stencil tests rely on 3.7 AST, do they need an upgrade? #8325 needs to be merged first.
- **Consensus**: #8325 needs to be merged first then we can merge the other PRs
- **Consensus**: Python 3.8 will be the oldest version of Python supported by 0.57
- Dropping support for win-32 and linux-32
- What does Anaconda distro support look like?
- How does this co-incide with dropping of Python support
- What about ARMv7 / AArch32? (Support statement needs an update, this is no longer supported)
- https://github.com/numba/numba/issues/7452
- **Consensus**: linux-32 to be removed from code immediately and from the Anaconda internal farm after 0.56.1
- **Consensus**: win-32 to removed also as per https://www.anaconda.com/blog/new-release-anaconda-distribution-now-supporting-m1
- Dropping support for old NumPy versions: https://github.com/numba/numba/pull/8283#issuecomment-1197505150
- Only 1.19 and upwards?
- Need to check on the platform support
- Will this have any implications
- **Consensus**: Runtime: 0.57 will support only 1.19 and above
- **Consensus**: Build time: probably ok to use 1.19 to build
- [Issue #8309](https://github.com/numba/numba/issues/8309) / [PR #8310](https://github.com/numba/numba/pull/8310) - CUDA: Atomic addition on complex components does not work in 0.56:
- This is a regression due to changes in [PR #7999](https://github.com/numba/numba/pull/8310), which made `array.real` and `array.imag` overloads
- This is a problem because:
- The overloads return arrays (not permitted in CUDA device functions)
- Overload can't be resolved from the low-level API (used to implement CUDA atomics)
- One fix (in PR #8310) is to revert this change and use the low-level API for `.real` and `.imag` again
- Pros: it works, and is a relatively simple fix
- Cons: it is a step backwards in the core implementation
- Another fix is to make overloads returning arrays work on CUDA
- Pros: Retains forward direction in the core, a general CUDA improvement
- Cons: Lots of work - needs NRT, change to call convention, and rewriting of many CUDA implementations as overloads
- Siu tried doing this but got stuck on (1) target_extension problems (active context not CUDA) and (2) `_generate_real_imag_attr.<locals>.intrin_typing.<locals>.codegen` (an `@intrinsic`) not found in lowering registry.
- Does a third way exist?
- **Resolution:** Merge #8310 after review for 0.56.1.
- Look into issues preventing it working with overloads afterwards
- [PR #8294](https://github.com/numba/numba/pull/8294): CUDA: Add trig ufunc support
- Works for the CUDA target, but adding simulator support is difficult.
- Does it need simulator support?
- Option 1: use [`__array_ufunc__`](https://numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__) to fix implementation.
- Option 2: Try making the "Fake within CUDA kernel array" an actual `ndarray` subclass.
- Option 3: If neither of the above approaches succeed, discuss whether simulator support is required again next week.
- Compilation speed benchmark - Luk

## 1. New Issues
- [#8303](https://github.com/numba/numba/issues/8303) - Allow multiple outputs for `guvectorize` on CUDA target
- [#8304](https://github.com/numba/numba/issues/8304) - Python 3.11
- [#8305](https://github.com/numba/numba/issues/8305) - Len of two concatenated Bytes objects is 0
- [#8307](https://github.com/numba/numba/issues/8307) - Ambiguous overloads are allowed for calls to jitted functions inside other jitted functions
- Needs poking, else it is operating in "surprise mode".
- [#8309](https://github.com/numba/numba/issues/8309) - Numba 0.56 does not support `atomic.add` on arrays of complexs anymore
- [#8311](https://github.com/numba/numba/issues/8311) - presence of while loop breaks literal_unroll compilation
- [#8314](https://github.com/numba/numba/issues/8314) - Numba not vectorizing 2d copy loop
- [#8317](https://github.com/numba/numba/issues/8317) - LoweringError
- [#8322](https://github.com/numba/numba/issues/8322) - Obsolete pycc script/code
- Answer is yes: please remove.

### Closed Issues
- [#8312](https://github.com/numba/numba/issues/8312) - numba's `np.full_like` is inconsistent with numpy's
- [#8313](https://github.com/numba/numba/issues/8313) - Segfault in nested loop with aliased array

## 2. New PRs
- [#8306](https://github.com/numba/numba/pull/8306) - Fix len of two concatenated Bytes objects bug
- [#8308](https://github.com/numba/numba/pull/8308) - CUDA: Support for multiple signatures
- [#8310](https://github.com/numba/numba/pull/8310) - CUDA: Fix Issue #8309 - atomics don't work on complex components
- [#8315](https://github.com/numba/numba/pull/8315) - Add get_local_mem_per_thread method to Dispatcher
- [#8316](https://github.com/numba/numba/pull/8316) - Fix error handling in float unboxing
- [#8318](https://github.com/numba/numba/pull/8318) - Cleanup old support for Python 3.6 and earlier code
- [#8319](https://github.com/numba/numba/pull/8319) - Bump minimum supported Python version to 3.8
- [#8320](https://github.com/numba/numba/pull/8320) - Add __name__ support for GUFuncs
- [#8321](https://github.com/numba/numba/pull/8321) - Fix literal_unroll pass erroneously exiting on non-conformant loop.
- [#8323](https://github.com/numba/numba/pull/8323) - Remove `mk_unique_var` from `array_analysis.py`
- [#8324](https://github.com/numba/numba/pull/8324) - Remove use of mk_unique_var in untyped_passes.py
- [#8325](https://github.com/numba/numba/pull/8325) - Remove use of mk_unique_var in stencil.py
- [#8326](https://github.com/numba/numba/pull/8326) - Remove `mk_unique_var` from `parfor_lowering.py`

### Closed PRs

## 3. Next Release: Version 0.57.0/0.40.0, RC Jan 2023