[WIP] Releasing the GIL in the inner loop of coordinate descent

- [x] release the GIL in the dual gap check (need to replace numpy calls by BLAS equivalents)
- [x] release the GIL in the sparse input variant
- [x] release the GIL for the precomputed Gram matrix variant
- [ ] benchmark the scaling for instance by hacking the cross_val_score function to make it possible to use the threading backend instead of multiprocessing
Why do you transpose Q here? It is supposed to be symmetric right? I am wondering whether the style of memory access (by row or by column) is not causing the slight performance regression we observe in your benchmarks.