MKLab-ITI/pygrank

Autotune too slow for backends other than numpy

maniospas opened this issue · 0 comments

Investigate why filter tuning can be exceptionally slow for backends other than numpy.

  • Focus on matvec first.
  • Slowness persists when L1 is used as optimization measure instead of AUC for matvec, investigate potentially slow operations (e.g., indexing?) in that project.

Related tests: None (change backends in the current compare_filter_tuning.py in the playground)