# Distributed.jl, DistributedArray.jl with InfiniBand cluster

**URL:** <https://discourse.julialang.org/t/distributed-jl-distributedarray-jl-with-infiniband-cluster/80049>\
**Category:** Julia at Scale\
**Tags:** cluster, distributed\
**Created:** [April 26, 2022, 11:56am UTC](https://discourse.julialang.org/t/distributed-jl-distributedarray-jl-with-infiniband-cluster/80049 "2022-04-26T11:56:29Z")\
**Posts on this page:** 1\
**Showing post:** 14

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**Author:** ![jishnub](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jishnub/32/33620_2.png) [@jishnub](https://discourse.julialang.org/u/jishnub)\
**Post date:** [April 26, 2022, 6:12pm UTC](https://discourse.julialang.org/t/distributed-jl-distributedarray-jl-with-infiniband-cluster/80049/14 "2022-04-26T18:12:17Z")

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This may not be the most optimal workflow. In particular, `Distributed` doesn’t offer a tree-based `reduce`, which might make `mapreduce` operations (ie. distributed for loops) seriously slow compared to MPI. It’s really hard to compare with MPI if one needs to transfer large data across cores.

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