[spacedome]

The person I was responding to seemed to have read my comment and thought I had an issue with raw performance. My point above is that writing iterative code that makes many LAPACK calls becomes difficult to write in Julia because there is no way to manage the memory in this situation other than ccall-ing everything yourself, at which point I would rather write FORTRAN. I work on eigenvalue solvers, so it is all more or less just wrapping a bunch of calls to BLAS/LAPACK. As you say, the main bottleneck is in the BLAS calls anyways, but the excess allocation in Julia can really slow you down. ARPACK is maybe a bad example because its all just mat-vec, when you need to do something like compute an SVD every iteration, thats where you run into issues.