My experience tells that computationally intensive codes are roughly 10% slower that their C++/Fortran counterparts. So fare we are implementing a data analysis

My experience tells that computationally intensive codes are roughly 10% slower that their C++/Fortran counterparts. So fare we are implementing a data analysis pipeline for a cosmological telescope (LSPE/STRIP), and we are migrating a number of codes my team developed for the Planck/LFI instrument. As data formats and a few details of the instrument changed, we decided to rewrite everything from scratch instead of manually fix and recheck every line of code, and we chose Julia 0.6. The 10% figure comes from a comparison of the performance of the codes whose logic has been left virtually unchanged, of course. What is awesome is the ability to run codes within Jupyter (like Python), and the ability to run "for" loops on large arrays fast and with little memory consumption (unlike Python). The 10% increase in computational time is fully absorbed by the reduced development/validation time!