BUG: Ugly fix for Apple's cblas_sgemv segfault #5223
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SGEMV in Accelerate framework will segfault on MacOS X version 10.9 (aka Mavericks) if arrays are not aligned to 32 byte boundaries and the CPU supports AVX instructions. This can produce segfaults in numpy.dot if we use numpy.float32 as dtype. This patch overshadows the symbols cblas_sgemv, sgemv_ and sgemv exported by Accelerate to produce the correct behavior. The MacOS X version and CPU specs are checked on module import. If Mavericks and AVX are detected the call to SGEMV is emulated with a call to SGEMM if the arrays are not 32 byte aligned. If the exported symbols cannot be overshadowed on module import, a fatal error is produced and the process aborts. All the fixes are in a self-contained C file and do not alter the _dotblas C code. The patch is not applied unless NumPy is configured to link with Apple's Accelerate framework.
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Ping @charris @juliantaylor @njsmith just to make sure you see this. |
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I'm listening ;) This is for Julian now. |
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@juliantaylor Are you going to put this in? |
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alright |
juliantaylor
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Oct 26, 2014
BUG: Ugly fix for Apple's cblas_sgemv segfault
This was referenced Oct 26, 2014
pv
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Nov 23, 2014
Corrects scipy.linalg.blas.sgemv, ARPACK, SuperLU similar to: numpy/numpy#5223
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SGEMV in Accelerate framework will segfault on MacOS X version 10.9
(aka Mavericks) if arrays are not aligned to 32 byte boundaries
and the CPU supports AVX instructions. This can produce segfaults
in numpy.dot if we use numpy.float32 as dtype. This patch overshadows
the symbols cblas_sgemv, sgemv_ and sgemv exported by Accelerate
to produce the correct behavior. The MacOS X version and CPU specs
are checked on module import. If Mavericks and AVX are detected
the call to SGEMV is emulated with a call to SGEMM if the arrays
are not 32 byte aligned. If the exported symbols cannot be
overshadowed on module import, a fatal error is produced and the
process aborts. All the fixes are in a self-contained C file
and do not alter the _dotblas C code. The patch is not applied
unless NumPy is configured to link with Apple's Accelerate
framework.