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library
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definition
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BLD: musllinux wheel build
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ENH: re-implement SIMD kernels of complex operations
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no performance gain with AVX512 enabled except for absolute
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access for complex load/store
summarized as follows:
64-bit contiguous partial load/store over 32-bit lane
npyv_load2_till_u32, npyv_load2_till_s32, npyv_load2_till_f32
npyv_load2_tillz_u32, npyv_load2_tillz_s32, npyv_load2_tillz_f32
npyv_store2_till_u32, npyv_store2_till_s32, npyv_store2_till_f32
128-bit contiguous partial load/store over 64-bit lane
npyv_load2_till_u64, npyv_load2_till_s64, npyv_load2_till_f64
npyv_load2_tillz_u64, npyv_load2_tillz_s64, npyv_load2_tillz_f64
npyv_store2_till_u64, npyv_store2_till_s64, npyv_store2_till_f64
64-bit non-contiguous load/store over 32-bit stride
npyv_loadn2_u32, npyv_loadn2_s32, npyv_loadn2_f32
npyv_storen2_u32, npyv_storen2_s32, npyv_storen2_f32
128-bit non-contiguous load/store over 64-bit stride
npyv_loadn2_u64, npyv_loadn2_s64, npyv_loadn2_f64
npyv_storen2_u64, npyv_storen2_s64, npyv_storen2_f64
64-bit non-contiguous partial load/store over 32-bit stride
npyv_loadn2_till_u32, npyv_loadn2_till_s32, npyv_loadn2_till_f32
npyv_loadn2_tillz_u32, npyv_loadn2_tillz_s32, npyv_loadn2_tillz_f32
npyv_storen2_till_u32, npyv_storen2_till_s32, npyv_storen2_till_f32
128-bit non-contiguous partial load/store over 64-bit stride
npyv_loadn2_till_u64, npyv_loadn2_till_s64, npyv_loadn2_till_f64
npyv_loadn2_tillz_u64, npyv_loadn2_tillz_s64, npyv_loadn2_tillz_f64
npyv_storen2_till_u64, npyv_storen2_till_s64, npyv_storen2_till_f64
2 channels de-interlave/interleave contiguous load/store for all data types
npyv_load_##sfx##x2, npyv_store_##sfx##x2
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New kernels provides better performance for non-contiguous
memory access and don't require 128-bit/64-bit aligment
for complex128/complex64 just 64-bit/32-bit, and implmented
via universal intrinics.
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shuffle intrinsics support 32-bit/64-bit vector data types,
unzip(deinterleave) intrinsics supports all data types.
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MAINT: Allow export/import of bools in dlpack
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Updates the header file and accept as well as export boolean now that
the header includes a definition for it.
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CI: Rebase NumPy compiled extension test modules on Cygwin
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This assumes NumPy is rebased before tests run,
but does not assume the locations are in the database.
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Also adjust CI so they don't immediately collide with NumPy.
I forgot to do that last time, which caused problems.
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This reverts commit 33709afdbbc47b7adb7dd06a730246d8c02f724f.
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This reverts commit 608864613b801b9c85573186a9d07eeac5e7e465.
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Forgot to check this earlier.
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Let's see if this fixes the 8-50 fork failures.
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`std::is_scalar` is defined in `type_traits`, which is missing from the
includes.
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Not adding a test since there is already a test that crashes due to
this, it just isn't used with weak promotion and right now I am
hoping I may be able to make the test suite runnable enabling it.
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Developer-Ecosystem-Engineering/simd_isnan_isinf_isfinite_signbit
ENH: Implement SIMD versions of isnan,isinf, isfinite and signbit
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reading random data and not reliable also didn't give additional coverage
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this new code into a new file
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On linux 32 an assert fires where stride (12) passed from ufunc_object (try_trivial_single_output_loop) to DOUBLE_isnan and DOUBLE_isfinite doesn't match the type size (8), we can relax this assert and instead fall back to the UNARY_LOOP path instead
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We don't see these failures but CI is hitting them, attempting to resolve
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Special case SSE
Fix PPC64 build
Only use vqtbl4q_u8 on A64
Stop trying to use optimizations on s390x
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Use reinterpret to support casting across many compiler generations
Resolve deprecation warnings
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NumPy has SIMD versions of float / double `isnan`, `isinf`, `isfinite`, and `signbit` for SSE2 and AVX-512. The changes here replace the SSE2 version with one that uses their universal intrinsics. This allows other architectures to have SIMD versions of the functions too.
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ENH: Add slots to NDArrayOperatorsMixin allowing them in subclasses
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I use the mixings in a few different file backed arrays.
However, the lack of slots make it difficult for me to use slots.
I mostly use slots to ensure that performance optimized code doesn't
create unecessary references to large chunks of memory.
If all parent classes do not have `__slots__` defined, I think that Python
(3.9) just ignores `__slots__` alltogether.
Thank you for considering.
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