Are you sure your systems are over determined? solve_ud in your stack trace says otherwise. Though you have solve_od too, and probably that’s nothing to do with the issue. But it doesn’t hurt to find why that’s happening and fix it if you think the systems should be od. Is armadillo solve() thread safe? That … Read more
UPDATE (30.07.2014): I re-run the the benchmark on our new HPC. Both the hardware as well as the software stack changed from the setup in the original answer. I put the results in a google spreadsheet (contains also the results from the original answer). Hardware Our HPC has two different nodes one with Intel Sandy … Read more
After some more googling I found the answer here. It turns out that certain Python modules (numpy, scipy, tables, pandas, skimage…) mess with core affinity on import. As far as I can tell, this problem seems to be specifically caused by them linking against multithreaded OpenBLAS libraries. A workaround is to reset the task affinity … Read more
The method numpy.show_config() (or numpy.__config__.show()) outputs information about linkage gathered at build time. My output looks like this. I think it means I am using the BLAS/LAPACK that ships with Mac OS. >>> import numpy as np >>> np.show_config() lapack_opt_info: extra_link_args = [‘-Wl,-framework’, ‘-Wl,Accelerate’] extra_compile_args = [‘-msse3’] define_macros = [(‘NO_ATLAS_INFO’, 3)] blas_opt_info: extra_link_args = [‘-Wl,-framework’, … Read more
BLAS is a collection of low-level matrix and vector arithmetic operations (“multiply a vector by a scalar”, “multiply two matrices and add to a third matrix”, etc …). LAPACK is a collection of higher-level linear algebra operations. Things like matrix factorizations (LU, LLt, QR, SVD, Schur, etc) that are used to do things like “find … Read more