I know that ATLAS is able to optimize itself for the machine it is compiled on and thus maximum benefits are found by compiling from source. Is there any benefit to compiling LAPACK from source? It would be much easier to just install the prebuilt package.
OpenBlas is quite fast, so you can link it to LAPACK. Have you tried precompiled version of LAPACK/BLAS from your CPU vendor? For example AMD ACML (free) or Intel MKL (free on linux for non-commercial and non-academic use)? You simply need to unpack and run install file.
In my opinion the only advantage of using ATLAS is then when you use some unusual CPU. Otherwise use the one from CPU vendor. Also there are nVIDIA CUDA and AMD OpenCL versions available which really rock.
EDIT: remember that you can always build an Ubuntu DEB package from source which is usually much easier than compiling software from source.
The repository package is not safe to use with threading due to the way it was compiled. I reported the bug on the Lapack forum, but it will take a long time for workarounds or solutions to trickle down into the repository. If you compile it yourself, be sure to add the "-frecursive" to gfortran.
In my experience, the best way to use blas/lapack on recent versions of ubuntu is to use the packaged openblas.
For what it's worth, I mostly use blas/lapack through python numpy/scipy, and using openblas speeds up some of the linear algebra by like 200x vs. the default. I've tried using custom ATLAS, but it was a huge pain and didn't give much if any speedup vs. openblas, but I might have been doing it wrong.