I've asked this in Cython mailing list but probably I should also get some feedback here too. I have the following function defined in Cython and using flat memory pointers to hold n by n array data. cdef some_C_layout_func(double[:, :, ::1] Am) nogil: # ... cdef double * work1 = <double*>malloc(n*n*sizeof(double)) cdef double *work2 = <double*> malloc(n*n*sizeof(double)) # ... # Lots of C-layout operations here # ... dgetrs(<char*>'T', &n, &n, &work1[0], &n, &ipiv[0], &work2[0], &n, &info ) dcopy(&n2, &work2[0], &int1, &Am[0, 0, 0], &int1) free(...) Here, I have done everything in C layout with work1 and work2 but I have to convert work2 into Fortran layout to be able to solve AX = B. A can be transposed in Lapack internally via the flag 'T' so the only obstacle I have now is to shuffle work2 which holds B transpose in the eyes of Fortran since it is still in C layout. If I go naively and make loops to get one layout to the other that actually spoils all the speed benefits from this Cythonization due to cache misses. In fact 60% of the time is spent in that naive loop across the whole function. Same goes for the copy_fortran() of memoryviews. I have measured the regular NumPy np.asfortranarray() and the performance is quite good enough compared to the actual linear solve. Hence whatever it is doing underneath I would like to reach out and do the same possibly via the C-API. But my C knowledge basically failed me around this line https://github.com/numpy/numpy/blob/8dbd507fb6c854b362c26a0dd056cd04c9c10f25... I have found the SO post from https://stackoverflow.com/questions/45143381/making-a-memoryview-c-contiguou... but I am not sure if that is the canonical way to do it in newer Python versions. Can anyone show me how to go about it without interacting with Python objects? Best, ilhan