Minutes_2021_06_08

Numba Meeting: 2021-06-08

Attendees: Siu, Graham, JimP, Stuart, ToddA, Val, Kalyan

NOTE: All communication is subject to the Numba Code of Conduct.

Low level @overload update
- side effects of changes and bugs discovered
  - target-context needs to be a singleton
  - bugs with instruction-set changing config
    - e.g. changes on SIMD-width
  - other bugs related with stack data returned across function boundary (thus stack lifetime expired)
Last week mainline was broken too
- bug was caused by "flags" added to the dispatcher caching keys, but the equals methods were id based so caches were invalidated on every re-execution.
JimP question on recursion:
- ndim=1, ndim>1, with ndim decreasing
- map_tuple https://github.com/numba/numba/pull/4813/files#diff-fea90b744bddcba2081c843dfb3fa990646969dd1996c3dfd989a083d778d7cdR557-R574
- generated_jit might be too old; might need to replace generated_jit to use overload internally.

#7089 - CUDA: Overloading existing implementations of functions seems not to work
#7086 - Initialize NUMBA_DEFAULT_NUM_THREADS with a batch scheduler aware value
#7085 - Vectorize doesn't play well with forceobj=True
- Usenumpy.vectorize instead
- forceobj=True should be disallowed with @vectorize
#7084 - Feature request: user-level statically defined tracepoints
- concern if static-trace works for JIT
- Graham testing out the idea but it is tricky due to the inlineasm trace-point requires switching ELF section
#7083 - CUDA_ERROR_OUT_OF_MEMORY with pinned_array
*** #7078 - Implement numpy.broadcast_to and numpy.broadcast_arrays
- Could provide some pointers to existing code
*** #7077 - Support full broadcasting in numpy.where
#7075 - Numba caches relative imports and not absolute