# Numba Meeting: 2024-03-05

Attendees:
FPOC (last week):
FPOC (incoming):

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

* Numba user survey update.
* 91 responses so far 🔥
* https://numba.discourse.group/t/numba-user-survey-2024/2411
* please continue to share 🙏
* Numba 0.59.1 schedule.
* Aim for patches in this week, test build over the weekend and release next week.
* Review NumPy 2.0 community communication note.
* How to stage NumPy 2.0 work and impact on maintenance.
* Draft of February's NAN is ready for comments from maintainers.
* Possible "solutions" to the question last week about how to specialise compilation based on a configuration object, and still preserve literals.

<details>

  from numba import jit, types
  from numba.extending import overload
  import functools


  # Some sort of configuration object
  class Config():
      def __init__(self, a, b):
          self._a = a
          self._b = b

      @property
      def a(self):
          return self._a

      @property
      def b(self):
          return self._b


  # Perhaps use a cache so that the identical Config instances return the same
  # jit function? This will prevent recompilation of the entry point for two
  # identical config instances as the jit function passed as the argument will be
  # the same.
  @functools.cache
  def obj2strkeydict(obj, config_name):

      # unpack object to freevars and close over them
      tmp_a = obj.a
      tmp_b = obj.b
      assert isinstance(config_name, str)
      tmp_force_heterogeneous = config_name

      @jit
      def configurator():
          d = {'a': tmp_a,
               'b': tmp_b,
               'config_name': tmp_force_heterogeneous}
          return d

      # return a configuration function that returns a string-key-dict
      # representation of the configuration object.
      return configurator


  # Define some "work"
  def work(pred):
      # ... elided, put python implementation here
      pass


  @overload(work)
  def ol_work(pred):
      assert isinstance(pred, types.Literal)
      print('Calling work with type', pred)
      return lambda pred: pred


  @jit
  def physics(cfig_func):
      # This is the main entry point to the application, it takes a configuration
      # function as an argument. It will specialise on each configuration
      # function.

      # call the function, config is a string-key-dict
      config = cfig_func()
      # unpack config, these types will be preserved as literals.
      a = config['a']
      b = config['b']
      # call some work to check the types.
      return work(b) + a


  # demo
  def demo():

      # Create two different Python based configuration objects with literal
      # entries.
      configuration1 = Config(10, True)
      configuration2 = Config(12.3, False)

      # Create corresponding converted configuration objects...
      jit_config1 = obj2strkeydict(configuration1, 'config1')
      jit_config2 = obj2strkeydict(configuration2, 'config2')
      # create "another" configuration1 instance, memoization prevents
      # duplication.
      jit_config1_again = obj2strkeydict(configuration1, 'config1')

      # Call the `physics` application, it will specialize on config.
      physics(jit_config1)
      physics(jit_config2)
      # should not trigger a 3rd compilation, config is memoized.
      physics(jit_config1_again)
      physics.inspect_types()


  if __name__ == "__main__":
      demo()

</details>

- LLVM upgrade:
- discourse thread: https://numba.discourse.group/t/llvmlite-upgrade-cadence/2436
- For 15: Passes not supported on legacy pass manager:
- ArgumentPromotionPass: https://reviews.llvm.org/D128536
- LoopUnswitchPass: https://reviews.llvm.org/D124376
- Effort to migrate to new pass manager estimates?
- ???
- Other tasks:
- llvmdev recipe update
- https://github.com/numba/llvmlite/tree/main/conda-recipes/llvmdev
- Required patch when building LLVM for testing:
- https://github.com/numba/llvmlite/blob/main/conda-recipes/llvm14-clear-gotoffsetmap.patch
- Beyond LLVM 15:
- Opaque pointer support
- Other changes?

## New "Ready for Review" PRs

## 1. New Issues
- [numba#9471](https://github.com/numba/numba/issues/9471) - CUDA crashes when passed complex record array
- [numba#9472](https://github.com/numba/numba/issues/9472) - [ANN] Numba User Survey 2024
- [numba#9473](https://github.com/numba/numba/issues/9473) - want `int64` rather than `Optional(int64)` when `dict.get(key, default_int64)` and dict value type is `int64`
- [numba#9475](https://github.com/numba/numba/issues/9475) - Infer Numba Types from Python Type Hints
- [numba#9476](https://github.com/numba/numba/issues/9476) - [feature request] assign a `tuple` to one row of `recarray`
- [llvmlite#1034](https://github.com/numba/llvmlite/issues/1034) - Add `numba` and `llvmlite` `musl` wheels for `alpine` support

### Closed Issues

- [numba#9477](https://github.com/numba/numba/issues/9477) - np.searchsorted no longer work for datetime64[ns] array type

## 2. New PRs
- [numba#9470](https://github.com/numba/numba/pull/9470) - Add ability to link CUDA functions with in-memory PTX.
- [numba#9474](https://github.com/numba/numba/pull/9474) - (Do Merge) NumPy 2: Removed dead code conflicting with binary builds against NumPy 2

### Closed PRs

(last numba: 9477; llvmlite 1034)

## 3. Short-term Roadmap

2024-gantt: TBD
2023-gantt: https://github.com/numba/numba/issues/8971