problems building numpy with ACML blas/lapack
Hello, I'm trying to build numpy from source to use AMD's ACML for matrix multiplication (specifically the multi-threaded versions gfortran64_mp). I'm able to successfully compile and use a working version of np.dot, but my resulting installation doesn't pass numpy's test suite, instead, I get a segfault. I'm hoping for some advice on what might be wrong... I'm on Debian, with a fresh install of Python-2.7.6. To install numpy, I've followed exactly the instructions previously posted to this list by Thomas Unterthiner. See http://numpy-discussion.10968.n7.nabble.com/numpy-ACML-support-is-kind-of-br.... The only thing I've adjusted is to try to use the gfortran64_mp version of ACML instead of just gfortran64. Using those instructions, I can compile numpy-1.8.0 so that it successfully uses the desired ACML libraries. I can confirm this by `ldd site-packages/numpy/core/_dotblas.so`, which shows that I'm linked to libacml_mp.so as desired. Furthermore, some quick timing tests indicate that for a 1000x1000 matrix X, calls to np.dot(X,X) have similar speeds as using custom C code that directly calls the ACML libraries. So, dot seems to work as desired. However, when I run numpy.test(verbose=4), I find that I get a seg fault ``` test_einsum_sums_cfloat128 (test_einsum.TestEinSum) ... Segmentation fault ``` Any ideas what might be wrong? From my benchmark tests, ACML is way faster than MKL or other options on my system, so I'd really like to use it, but I don't trust this current install. Thanks! - Mike
participants (2)
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Michael Hughes
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Thomas Unterthiner