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Minutes_2019_08_08

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Siu Kwan Lam edited this page Aug 13, 2019 · 2 revisions

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

0. Feature Discussion

  • Need to build a new llvmdev at some point soon... anyone want anything else?

    • Polly needs adding (*)
    • gcov needs adding
    • Random stuff for grail quest needs adding
      • patches not accepted into mainline
      • OrcJit-only patches (* maybe moving to OrcJit)
    • compiler-rt needs resolving
    • adding in the ref count pruner at LLVM level needs adding
    • that label length patch needs removing(?)
    • LLVM 8.0.1 patch release (*)

    footnote: (*) required

  • JIT modules

  • Performance of calling methods on jitclass objects: https://github.com/numba/numba/issues/2166

    • Request from Pandas developers (they see 15x slowdown compared to calling a function)
    • Related to insufficient reference count pruning

  • Label length patch - stay or go?

  • boundschecking: https://github.com/numba/numba/issues/730

    • Siu: put some pointers to where to modify
      • in numba/cgutils.py: get_item_pointer2
      def get_item_pointer2(builder, data, shape, strides, layout, inds,
                      wraparound=False):
    if wraparound:
    • Siu: pointer to how to raise exception in builder land

  • Pearu wants to work on unicode in arrays #4018

1. New issues

  • #4406 - numba.errors.NotDefinedError: Failed in object mode pipeline (step: analyzing bytecode) Variable '$6.2' is not defined.
    • likely not a good application for Numba
  • #4405 - CUDA functions can be compiled but not called with Tuple and UniTuple arguments
    • should be easy to fix?
  • #4404 - Support keyword arguments with guvectorize
    • blocked because NumPy gufuncs objects don't take kwargs
  • #4403 - Cannot lower constant bytes.
    • opened PR #4408 to fix
  • **** #4402 - meta issue: fp16 support
    • Is this also of interest for recent CPU targets?
  • #4401 - Named Tuple in objmode context
    • Limitiation of object mode block that should be lifted
  • #4400 - numba.errors.UnsupportedError: Failed in nopython mode pipeline (step: analyzing bytecode) Use of unsupported opcode (SETUP_EXCEPT) found
    • unsupported try except usage
    • Could we have a better error message?
  • #4399 - Numpy corrcoef performance is better without Numba
    • np.cov needs a review
  • #4398 - CUDA simulator mode does not recognize customized numpy dtype array
    • open PR to fix (#4410)
  • #4396 - Turn off function name numbering for easier caching for CUDA
    • instead, let exact function name be specified
  • **** #4395 - fp16 GPU support
    • See above
  • #4392 - Issue lowering ir.StaticSetItem
    • Can't figure out what the issue is from user-provided information
    • **** aside: does ir.StaticSetItem even need to exist now that we have literal types?
      • Static{G,S}etItem is ignoring
  • #4389 - Ahead-of-time compilation for cuda.jit
    • Will be helped by being able to set explicit name for generated function
    • Will need docs and/or better ABI for passing NumPy arrays
  • #4387 - Jitting linked list takes an unexpectedly long time
    • How does this even compile??
    • We should raise an error message
  • #4385 - Default argument gives "this should not have happened" error
    • We should forbid usage of mutable types as defaults
    • this specific case is failing due to reflected list
  • #4382 - Default argument with typed list?
    • see above

Already Closed

  • #4394 - NotImplementedError: (<class 'numba.ir.StaticSetItem'>, heatmap.1[(slice(None, None, None), slice(None, None, None), -1)] = $151.17)
  • #4393 - report a bug
  • #4391 - Hello. I am ru
  • #4390 - CUDA kernel slowdown after several thousand kernel iterations
  • #4384 - [duplicate bug, please delete] Default argument can cause segmentation fault
  • #4381 - Numba 0.45.1 checklist

2. Open PRs

  • #4410 - Fix #4111. cudasim mishandling recarray
  • **** #4409 - Add exception chaining for better error context
  • #4408 - Add lowering for constant bytes.
  • **** #4407 - Restore the "free" conda channel for NumPy 1.10 support.
    • Should discuss whether we should drop some NumPy's from testing?
    • for reference, dask's min numpy is 1.13
  • #4397 - Add note about np.sum
  • **** #4388 - Update Python 2.7 EOL statement
    • !!!! more discussion next week !!!!
  • #4386 - Implement np.count_nonzero

Already merged

  • #4383 - Change log update for 0.45.1

4. Next Release: Version 0.46.0, RC=Sept 30

  • Finish pending rewrite passes
  • Python 3.8 support
  • Document best practices for constructing new compiler pipelines
  • Define autodiscovery system for Numba extensions (like numba_scipy or HPAT) that don't need direct user import
  • Allow opt-in dispatching of functions by literal value
  • Making caching aware of transitive dependencies
  • Define declarative typing system for @overload (to be used in future releases)
  • Numba Runtime C API for extensions to register reference-counted memory with the runtime.
  • Start using new CI system in parallel with existing one
  • Priority bug fixes:
    • Low performance of JIT method calls (requested by Pandas devs)
    • Others TBD
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