# Writing to CSV files and plotting are getting slower in every run

**URL:** <https://discourse.julialang.org/t/writing-to-csv-files-and-plotting-are-getting-slower-in-every-run/68711>\
**Category:** General Usage\
**Tags:** question\
**Created:** [September 24, 2021, 5:01pm UTC](https://discourse.julialang.org/t/writing-to-csv-files-and-plotting-are-getting-slower-in-every-run/68711 "2021-09-24T17:01:56Z")\
**Posts on this page:** 1\
**Showing post:** 16

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**Author:** ![Marc.Cox](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marc.cox/32/7514_2.png) [@Marc.Cox](https://discourse.julialang.org/u/Marc.Cox)\
**Post date:** [October 2, 2021, 5:49pm UTC](https://discourse.julialang.org/t/writing-to-csv-files-and-plotting-are-getting-slower-in-every-run/68711/16 "2021-10-02T17:49:18Z")

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> [@fksayaci](#):
>
> I also wanted to share a warning with you. I have no idea if this is related to slow processing of outputs, but I just wanted to share:

Yes, **you’ll need to confirm** this is the issue,  
but it seems that your Warning message points to this SOLN \>\>  
[MKL+PyPlot](https://discourse.julialang.org/t/mkl-pyplot/22581)

```julia
Error: .. Warning: both Julia and NumPy are linked with MKL, which may cause conflicts and crashes (#433).
└ @ PyCall .. .julia\packages\PyCall\ .. \src\numpy.jl

```

The kernel appears to have died. It will restart automatically.

And the real underlying problem seems to possibly be that essentially julia recompiles mkl from source per this \>\>

> Is it possible to link julia to mkl without recompiling from source?

> [@Is it possible to link julia to mkl without recompiling from source?](https://discourse.julialang.org/t/is-it-possible-to-link-julia-to-mkl-without-recompiling-from-source/31752):
>
> Is it possible to use mkl instead of blas without having to recompile julia from source? For instance, through some configuration file where we can point where the mkl lib is on the system and then julia can recognise and utilise that? I’ve found [this](https://github.com/JuliaComputing/MKL.jl) but I’m not sure about the following: 1.) Where is mkl gonna be downloaded and installed? 2.) I have julia installed in a conda-env. Does that make a difference? 3.) If I install conda mkl in the same conda-env that I have julia installed would…

NOTE Julia uses OpenBLAS, and MATLAB (& Python ?) uses MKL.  
The easiest way to use Julia is probably here \>\>

> [@Computational Speed](https://discourse.julialang.org/t/computational-speed/22572/2):
>
> Julia uses OpenBLAS, and MATLAB uses MKL. The easiest way to use Julia with MKL is probably: EDIT: BLAS is multithreaded. If BLAS is using 8 threads, CPU time will elapse at about 8 CPU seconds per second. Therefore, be sure to actually time both in the same way. For example, simply @time begin MM=rand(Float64,10000,10000) mmm=inv(MM) end

… So **IF you confirm** you actually ARE recompiling MKL over and over (Ugh 😬 ) ;  
At a Birds Eye View it Appears your bottom line options may be :  
– add OpenBLAS and then using OpenBLAS , and confirm reconfiguration has stopped Julia calling MKL to stop recompiling MKL etc.  
– add Plots and then using Plots , Ditch PyPlot PyCall to stop recompiling MKL

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