Minutes_2019_10_15
Attendees: Ehsan, Pearu, Val, Stan, Siu, Todd, Stuart, Aaron
-
Numba 0.46.1
- Python 3.8 was released on Monday
- Critical bugs
- inliner bug @stuart
-
Copied from last 2 weeks
- 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
-
- Requests for 0.47 (last release for the year)
-
#4702 - Problem installing numba 0.46 and numpy 1.17 in conda
- we still build with numpy pinned in farm
- official builds are not affected
- new CI farm is not affected
-
#4701 - Numerical differences when using numpy.linalg.norm
- numpy bug?
- add FAQ entry on floating point precision
-
**** #4698 - Serializing typed Lists and Dicts?
- pickle-able Lists and Dicts make sense
- Stan thinks:
- focus on portability not performance
- make pickle work
- Val thinks:
- require reasonable performance
- Need more thinking.
-
#4697 - nested dictionaries failed
-
**** #4696 - Inconsistent behavior of
stencil
decorator- parfors
- Todd will investigate
-
#4694 - Default
None
argument treatment is different in type-inference and in lowering -
IR inliner/rewrite-passes related
- Issues:
- **** #4693 - raising Exceptions in inlined functions fails on compile
- Need to introduce pass grouping?
- **** #4692 - generated_jit doesn't get passed correct arguments with inline='always' argument
- Should generated_jit be deprecated in favor of overload?
- or just merge the impl
- Should generated_jit be deprecated in favor of overload?
- **** #4691 - tuple slicing fails in inlined functions
- **** #4693 - raising Exceptions in inlined functions fails on compile
- Action
- Issues are not critical
- Do better fix as suggested by Ehsan
- better separation of analysis vs transformation
- Issues:
-
#4690 - numba 0.46, useless(?) code in parfor init block
- waiting for reproducer
-
**** #4689 - The fastmath SVML option has been broken since somewhere between 0.43.1 and 0.44
- initializing the LLVM engine before SVML option is set will freeze the no-SVML setting for subsequent compilations
- not detected by unit tests because compile_isolated creates a new LLVM engine every time
-
#4688 - Add support for @classmethod in jitclass
- put on the jitclass wishlist
-
#4687 - inline_closure_call may ignore glbls parameter
- replied
-
#4684 - Missing arg in TypeError
-
#4683 - Help for VMProf with Numba
- need more information
- maybe point them to numba/stacktrace?
-
#4681 - Error in
py_call_impl(callable, dots$args, dots$keywords)
- need more info
-
#4679 - Numba 0.46.0 final checklist
- closed
-
**** #4676 - CUDA decorator/compiler argument to set literal precision
- need to fix the integer case in 0.47
- see NBEP
- what about float?
- literal only?
-
__truediv__(int, int)
ormath.log(int)->float?
? - what does C do?
- need to fix the integer case in 0.47
-
#4674 - Same code works on macOS Python 3.7.2 but not on Ubuntu 16.04 Python 3.5.2
- Siu looking into to this.
-
Linuxpy3.5 only bug so far. - problem in IR mutation in parfor transformation
-
#4671 - can't pickle DUFunc objects
- see PR
-
#4670 - I have a question about half precision float support in numba cuda
- only just started
-
#4668 - Round segfaults in cuda jitted function if passed ndigits optional parameter
- need to catch this in typing
- #4700 - Numba makes Large Numbers Negative
- #4699 - Request for f-string support and numpy.isscalar.
- #4686 - Cannot parallelize a loop
- #4678 - @jit decorator with signature including List(List(int64)) not working
- #4703 - Fix numba.jit parameter name signature_or_function
- **** #4695 - Refactor
overload*
and supportjit_options
andinline
- #4677 - Add support for np.setxor1d
- #4673 - Extend test timeout and add identifier cmdline
- #4672 - Fix pickling of dufunc
- #4669 - Add link to ParallelAccelerator paper.
- #4685 - Apply #4682 to 0.46 release branch
- #4682 - Update changelog for 0.46 final release
- #4680 - Apply #4675 to on 0.46 release branch
- #4675 - Bump cuda array interface to version 2
- CPython 3.8