# Fast LogSumExp over 4th dimension

**URL:** <https://discourse.julialang.org/t/fast-logsumexp-over-4th-dimension/64182>\
**Category:** Performance\
**Tags:** tullio, loopvectorization\
**Created:** [July 6, 2021, 11:21pm UTC](https://discourse.julialang.org/t/fast-logsumexp-over-4th-dimension/64182 "2021-07-06T23:21:30Z")\
**Posts on this page:** 1\
**Showing post:** 9

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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:** [July 7, 2021, 2:05pm UTC](https://discourse.julialang.org/t/fast-logsumexp-over-4th-dimension/64182/9 "2021-07-07T14:05:50Z")

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> [@jroon](#):
>
> I think then I’ll have to stick with my R version. Unless there is more improvement to be found here, the overhead of moving data from R to Julia which isn’t captured in my R benchmarks above that erases small benefits

In general, you won’t get much benefit from Julia from moving a single vectorized function to Julia. See also this post: [Comparing Numba and Julia for a complex matrix computation - #3 by stevengj](https://discourse.julialang.org/t/comparing-numba-and-julia-for-a-complex-matrix-computation/63703/3)

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