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Valentin Haenel edited this page Mar 30, 2020 · 1 revision

Numba Meeting: 2019-11-04

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

0. Feature Discussion

  • Py3.8 patch #4755

    • no patch release. because too much changes
    • plan to release with 0.47 end-of-year release
  • pytest/CI

    • pytest changes on hold (25% test throughput penalty on 4 cores compared to current test runner)
    • git-diff and sliced testing port to current numba.runtests
    • faster public CI turn-around; 10ish mins
  • Y2020 Q1 Spring cleaning project board:

  • bitshifting: https://github.com/numba/numba/issues/4786

    • numpy: (np.uint64(255) << 1) -> TypeError
    • again, np-integer vs py-integer casting rules

1. New issues

  • #4778 - Numba v0.46.0 issue when executing umap
  • #4775 - Malloc Error when checking array flag
    • bug on arr.flags
  • #4773 - jitclass member array assignments do not type promote
    • probably setattr not promoting
  • **** #4772 - String rjust/ljust/center slow to compile
    • compiler performance
    • Things to look at:
      • big IR?
      • compiling same thing over again?
    • Discussion:
      • not much benefit to on-the-fly compilation
      • consider shipping pre-compiled code
  • #4771 - is there any method to calculate massive regression by the power of GPU?
  • #4765 - Error initialising ROC
  • #4764 - Adding a transposed tensor gives unexpected (wrong) result
  • #4763 - Numba 0.46.0 depends on pkg_resources but does not declare a setuptools dependency
    • conda meta.yaml fix
  • #4761 - Build time/pip package. Using PyPy7.2 with Python level 3.6 doesn’t means using Windows.
  • #4759 - NUMBA_DISABLE_JIT breaks @generated_jit
    • another reason for coverage support
  • #4758 - convert float to string within numba function
    • not easy to do
  • #4754 - Passing a Tuple of Numpy arrays to cfunc (scipy.integrate.quad)
    • Siu will take a look
    • structure cast
  • #4752 - Profiling only the running time of a jit-decorated function
    • save compilation time

Already Closed

  • #4774 - Can't unify return type from the following types: array(float32, 2d, C), array(float64, 2d, C)
  • #4766 - how to use the jitclass without the spec parameter
  • #4757 - numba-RNG in 2D threads

2. New Open PRs

  • #4780 - [WIP] Implement np.gcd and math.gcd
  • #4779 - Make slice constructor more similar to python.
  • **** #4777 - Fix transpose output array layout
    • it should not need to degrade to A layout
    • Siu will look
  • #4776 - Fix and enable idle tests from test_array_manipulation
  • #4770 - Update np.arange implementation with @overload
    • Need to check compile_isolated
  • #4769 - [WIP] objmode complex type spelling
  • #4768 - Update typeconv error message to ask for sys.executable.
  • #4760 - Make type_callable return the decorated function
  • **** #4756 - Add parfor support for ndarray.fill.
    • Todd will simplify it a little with find_call_name
    • Further clean up and move to new style in springcleaning
  • **** #4755 - Python3.8 support
  • **** #4753 - [WIP] pytest

Already merged/closed

  • #4767 - pin to 2.7.16 for py27 on osx
  • Closed #4762 - fix Python 2.7 on MacOSX build issue (second attempt)

4. Next Release: Version 0.47.0, RC=December 19

  • 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

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