# Strange multithreaded behaviour on ARM Ampere A1 (Oracle Cloud)

**URL:** <https://discourse.julialang.org/t/strange-multithreaded-behaviour-on-arm-ampere-a1-oracle-cloud/79490>\
**Category:** Julia at Scale\
**Tags:** multithreading, aarch64, arm\
**Created:** [April 14, 2022, 2:01pm UTC](https://discourse.julialang.org/t/strange-multithreaded-behaviour-on-arm-ampere-a1-oracle-cloud/79490 "2022-04-14T14:01:11Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![mkitti](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mkitti/32/12459_2.png) [@mkitti](https://discourse.julialang.org/u/mkitti)\
**Post date:** [April 14, 2022, 2:36pm UTC](https://discourse.julialang.org/t/strange-multithreaded-behaviour-on-arm-ampere-a1-oracle-cloud/79490/3 "2022-04-14T14:36:17Z")

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> [@Dandan](#):
>
> `eigen`

You might also want to look at `BLAS.set_num_threads` to set the number of threads that OpenBLAS is trying to uses:

> [@Julia Threads vs BLAS threads](https://discourse.julialang.org/t/julia-threads-vs-blas-threads/8914):
>
> I am experimenting with Julia’s (experimental) multithreading feature recently and like the results so far. One of the problems I need to deal with, involves the multiplication of several pairs (say in the order 5 to 50) of matrices, whose size is average (say linear size in the order 10 - 1000). For that problem, there can be a competition between either using Julia threads (to loop over the different pairs) versus using multithreaded matrix multiplication provided by BLAS, and it will depend …

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