Why is B = numpy.dot(A,x) so much slower looping through doing B[i,:,:] = numpy.dot(A[i,:,:],x) )?

Question

I\'m getting some efficiency test results that I can\'t explain.

I want to assemble a matrix B whose i-th entries B[i,:,:] = A[i,:,:].dot(x), where each A[i,:,:] is

Answer 1

numpy.dot only delegates to a BLAS matrix multiply when the inputs each have dimension at most 2:

#if defined(HAVE_CBLAS)
    if (PyArray_NDIM(ap1) <= 2 && PyArray_NDIM(ap2) <= 2 &&
            (NPY_DOUBLE == typenum || NPY_CDOUBLE == typenum ||
             NPY_FLOAT == typenum || NPY_CFLOAT == typenum)) {
        return cblas_matrixproduct(typenum, ap1, ap2, out);
    }
#endif

When you stick your whole 3-dimensional A array into dot, NumPy takes a slower path, going through an nditer object. It still tries to get some use out of BLAS in the slow path, but the way the slow path is designed, it can only use a vector-vector multiply rather than a matrix-matrix multiply, which doesn't give the BLAS anywhere near as much room to optimize.