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What's the state of the art with regards to getting numpy to use mutliple cores (on Intel hardware) for things like inner and outer vector products, vector-matrix multiplications etc? I am happy to rebuild numpy if necessary, but at this point I am looking at ways to speed things up without changing my code. For reference, my show_config() is as follows, and I've never observed numpy to use more than one core: atlas_threads_info: libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/local/atlas-3.9.16/lib'] language = f77 include_dirs = ['/usr/local/atlas-3.9.16/include'] blas_opt_info: libraries = ['ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/local/atlas-3.9.16/lib'] define_macros = [('ATLAS_INFO', '"\\"3.9.16\\""')] language = c include_dirs = ['/usr/local/atlas-3.9.16/include'] atlas_blas_threads_info: libraries = ['ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/local/atlas-3.9.16/lib'] language = c include_dirs = ['/usr/local/atlas-3.9.16/include'] lapack_opt_info: libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/local/atlas-3.9.16/lib'] define_macros = [('ATLAS_INFO', '"\\"3.9.16\\""')] language = f77 include_dirs = ['/usr/local/atlas-3.9.16/include'] lapack_mkl_info: NOT AVAILABLE blas_mkl_info: NOT AVAILABLE mkl_info: NOT AVAILABLE

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