Comments (4)
I'm really out of my depth with this and I'm not sure @tbenthompson has the time to work on this. Is there someone that could take care of this from the engineering team @xhochy?
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Maybe @cbourjau can do this ;)
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I'm a bit confused why this would make a difference. It is my understanding that on MacOS, jemalloc is built with --with-jemalloc-prefix="je_"
by default. And it's on MacOS, where we're running into the error. Why would building the conda package with this prefix change this? Isn't jemalloc already namespaced on MacOS?
from tabmat.
The issue here is that loading a default jemalloc
build also overloads C++'s new
and delete
operators. In the above trace, we see a C++ deallocation that was previously done with the system allocator (see also jemalloc/jemalloc#1701) . We can workaround this issue by using a namespaced version of jemalloc
that also doesn't override the C++ allocator by default. I'm adding such a jemalloc
build as part of conda-forge/jemalloc-feedstock#25 with the package name jemalloc-local
.
from tabmat.
Related Issues (20)
- Cannot sandwich SplitMatrix with non-owned array
- dlopen symbol not found issue with M1 wheel HOT 3
- `-march` is not cross-platform HOT 1
- Daily run failure: Unit tests HOT 1
- Sandwich product fails for very large dense matrices
- Sandwich product fails for large F-contiguous matrices
- Cross sandwich product fails for split matrices with large dense matrix part
- wheel build for aarch64 is incredibly slow HOT 1
- Equality comparison is incorrect for `CategoricalMatrix` HOT 4
- `matvec` inconsistent behavior when used with the `col` argument. HOT 3
- `.getcol` method ignores `drop_first` attribute
- `SplitMatrix.__init__()` does not handle `SplitMatrix` inputs correctly
- Sandwich product fails for large F-contiguous matrices in 3.1.8 HOT 1
- Daily run failure: Unit tests
- Installing with Pip on Mac leads to ImportError HOT 6
- Missing Linux x86_64 wheels for version 3.1.12
- Daily run failure: Unit tests HOT 2
- Create SplitMatrix from polars data frame
- Daily run failure: Unit tests
- Replace `scipy.sparse.*_matrix.A` by `scipy.sparse.*_matrix.toarray()`
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