Fastest way to obtain the singular value decomposition of a bidiagonal matrix (using LAPACK)?

I am looking to find the fastest code/algorithm/package for obtaining the singular value decomposition (SVD) of a real, square bidiagonal matrix. The matrices I am working with are fairly small - typically somewhere around 15x15 in size. However, I am performing this SVD thousands (maybe millions) of times, so the computational cost becomes quite large.

I am writing all of my code in C. I assume the fastest codes for performing the SVD will probably come from LAPACK. I have been looking into LAPACK and it seems like I have quite a few different options for performing the SVD of real, bidiagonal matrices:

1. dbdsqr - uses a zero-shift QR algorithm to get the SVD of a bidiagonal matrix

2. dbdsdc - uses a divide and conquer algorithm to get the SVD of a bidiagonal matrix

3. dgesvd - not sure exactly how this one works, but it can handle any arbitrary matrix, so I assume I am better of using dbdsqr or dbdsdc

4. dgesdd - not quire sure how this one works either, but it can also handle any arbitrary matrix, so I assume I am better of using dbdsqr or dbdsdc

5. dstemr - estimates eigenvectors/eigenvalues for a tridiagonal matrix; I can use it to estimate the left singular vectors/values by finding the eigenvectors/values of A*A'; I can then estimate the right singular vectors/values by finding the eigenvectors/values of A'*A

6. dstemr - perhaps there is an even faster way to use dstemr to get the SVD... please enlighten me if you know of a way

I have no idea which of these methods is fastest. Is there an even faster way to get the SVD of a real, bidiagonal matrix that I haven't listed above?

Ideally I am looking for a small example C code to demonstrate the fastest possible SVD for a relatively small real, bidiagonal matrix.