Dear list, so far I used Enthoughts Python Distribution which contains a compiled version of numpy linked against MKL. Now, I want to implement my own extensions to numpy, so I need my build numpy on my own. So, I installed Intel Parallel studio including MKL and the C / Fortran compilers. I linked against the same libraries as Enthought: In [2]: np.show_config() lapack_opt_info: libraries = ['mkl_lapack95_lp64', 'iomp5', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pthread'] library_dirs = ['/opt/intel/lib/intel64', '/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/include/'] blas_opt_info: libraries = ['iomp5', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pthread'] library_dirs = ['/opt/intel/lib/intel64', '/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/include/'] lapack_mkl_info: libraries = ['mkl_lapack95_lp64', 'iomp5', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pthread'] library_dirs = ['/opt/intel/lib/intel64', '/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/include/'] blas_mkl_info: libraries = ['iomp5', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pthread'] library_dirs = ['/opt/intel/lib/intel64', '/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/include/'] mkl_info: libraries = ['iomp5', 'mkl_def', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pthread'] library_dirs = ['/opt/intel/lib/intel64', '/opt/intel/mkl/lib/intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/intel/include/'] and used the intel compilers to build numpy. Since I have an i7 processor I compiled including AVX instructions. Yet, EPD is double as fast as my own build executing the simple benchmark from: http://dpinte.wordpress.com/2010/01/15/numpy-performance-improvement-with-th... I expected at least comparable performance. How is such a decrease possible? Did I miss a significant part to make numpy really fast? Thanks Christoph