# Keeping scientific objectivity and details in benchmark reports

**URL:** <https://discourse.julialang.org/t/keeping-scientific-objectivity-and-details-in-benchmark-reports/63579>\
**Category:** Community\
**Tags:** question\
**Created:** [June 25, 2021, 8:54pm UTC](https://discourse.julialang.org/t/keeping-scientific-objectivity-and-details-in-benchmark-reports/63579 "2021-06-25T20:54:19Z")\
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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [June 25, 2021, 9:50pm UTC](https://discourse.julialang.org/t/keeping-scientific-objectivity-and-details-in-benchmark-reports/63579/5 "2021-06-25T21:50:19Z")

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> [@mbauman](#):
>
> I don’t think Erik or Alan are making any claims that the algorithms are exactly the same or that Julia has 3x better performance than Fortran _on average_ .

I think it’s similar to [this discussion](https://discourse.julialang.org/t/julia-vs-fortran-complaint/4366), and see in particular [this comment](https://discourse.julialang.org/t/julia-vs-fortran-complaint/4366/9):

> Obviously, it was a situation in which someone ported some low-level kernel from Fortran to Julia, discovered some additional optimizations along the way (possibly made easier by Julia), and ended up with a 30% speedup. This kind of thing is not unreasonable — it happens all the time _if you have a language in which you can write high-performance code_ .

Moreover, that thread goes into several examples of ways in which Julia often makes it particularly easy to implement performance optimizations that are awkward (but of course not impossible) in Fortran.

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