Minutes_2019_10_29

Numba Meeting: 2019-10-29

Attendees: Siu, Aaron, Ehsan, Stuart, Todd, Val, James

0. Feature Discussion

• Numba 0.46.1 Status
  • Python 3.8 bytecodes really mess with control flow assumptions
  • WIP PR: https://github.com/numba/numba/pull/4755
  • looplifting is completely broken
  • tuple hash algo changed
  • good news: 90+% of testsuite passes
• pytest
  • platform specific issues
    • pytests bugs
    • non blockers
  • IR legalizer catching a lot of issues (reported as warnings)
  • test slicing/selection works
• Opportunity for Aaron, Pearu, and James to work on implementing functions as first-class types #3405 (IIUC currently each function is treated as it's own unique type)
  • How large an task do we suspect this is?
  • If a large undertaking, is there a related smaller task that would still be useful to Numba?
  • related issue:
    • https://github.com/numba/numba/issues/2542
    • https://github.com/numba/numba/issues/2952
    • https://github.com/numba/numba/issues/2956
  • tasks:
    • Signature type
    • Runtime value of a Function
      • a pointer to the actual function

1. New issues

• #4751 - Import numba crashes after import s3fs
  • pyarrow problem
• #4750 - jitting a jitted function raises AttributeError
  • add warnings, "Don't @jit @jit"
• **** #4749 - Allow casting numpy scalars
  • should we allow implicit cast from Array scalar to scalar?
    • thought 1: No. because performance linter
    • thought 2: Yes. too easy to have a array scalar i.e. a_numpy_array[idx] can yield array scalar
    • doesn't sound dangerous. should try
• **** #4748 - Parallel failing in no python mode
  • slicing with a 0-d array as index
• #4747 - type unification problem
• #4746 - Support numpy.indices
• **** #4742 - problem with jitted function when integer indexing with zero element
  • parfor
• #4740 - Structured array of jitclass instances (jitclass <-> dtype)
• #4739 - AOT always installed to site-package folder not the module folder
• **** #4738 - Lowering Error when using @njit(parallel = True)
  • parfor
  • should have better error message
• #4735 - LoweringError when yielding multiple elements from an njit function
  • tuple typing issue
• #4733 - Python 3.7 warns on from collections import Iterable
• #4732 - 'cannot import name '_typeconv' from 'numba.typeconv'
• #4731 - .strip() not stripping \t.

Already Closed

• #4745 - "Call to cuPointerGetAttribute" errors with multi-process + multi-GPU + external GPU arrays
• #4743 - question about the efficientcy of numba.typed.dict

2. New Open PRs

• #4744 - Fix_Py27_Build_Azure
• #4741 - Update choosing the "CUDA_PATH" for windows
• #4734 - prefer to import from collections.abc

Already merged/closed

• #4737 - remove maxParallel from Azure Pipelines
• #4736 - fix flake8 errors

4. Next Release: Version 0.46.1, RC=

• CPython 3.8

• Requests for 0.47 (last release for the year) - jitclass performance issues - llvm 9 trial - CTK libcudadevrt.a - CI needs to take 50% of current time - Val & Stu already looking at this - also checking Azure CI config to avoid wasting compute time - Caching: - transitive dependency - other issues: i.e. function as argument, with objmode - distributing cache - Immutable list and deprecating reflected list - Switch to pytest (see above) - Using Numba to generate LLVM/NVVM IR for different targets https://github.com/numba/numba/issues/4546 - @overload for gpu