# Multithreaded code on beefy computer runs just as fast as serial code on M1 Mac

**URL:** https://discourse.julialang.org/t/multithreaded-code-on-beefy-computer-runs-just-as-fast-as-serial-code-on-m1-mac/75210
**Category:** Performance
**Tags:** performance, parallel, differentialequation
**Created:** [January 26, 2022, 2:30am UTC](https://discourse.julialang.org/t/multithreaded-code-on-beefy-computer-runs-just-as-fast-as-serial-code-on-m1-mac/75210 "2022-01-26T02:30:17Z")
**Posts on this page:** 1
**Showing post:** 25

<div class="post-metadata">

### Author: ![ash](https://avatars.discourse-cdn.com/v4/letter/a/f19dbf/32.png) [@ash](https://discourse.julialang.org/u/ash)
#### Post date: [January 26, 2022, 5:50pm UTC](https://discourse.julialang.org/t/multithreaded-code-on-beefy-computer-runs-just-as-fast-as-serial-code-on-m1-mac/75210/25 "2022-01-26T17:50:34Z")

</div>

Thank you for the advice Chris, this worked amazingly well.

Using `@btime`, the original code (UMF + `algebraicmultigrid`) took 10.20s. Changing to `KrylovJL_GMRES()` it takes 5.293s. Changing from `algebraicmultigrid` to `imcompletelu` it takes 590 ms!. I played around with \tau, using values from 0.1 to 10,000 and the 50 from the example works perfectly. All the results above use `tspan=(0.0,1.0)`.

I am sticking to `KenCarp4` since I plan to split the equation ASAP and will post the results.

Here’s the profile for the most optimized case, running it 100 times. I am still not very good at reading these graphs so let me know if you catch anything.

 ![CleanShot 2022-01-26 at 09.47.22](https://global.discourse-cdn.com/julialang/original/3X/4/c/4c1717cb304fe09bf9ec193786e5731011c8d547.png)

I can tell for example that there’s a lot of time spent copying (`copyto!`) and broadcasting (`materialize!`). `BLAS.axpy!` and `BLAS.dot` are also significant, so I can work on optimizing BLAS.

The next step for now is splitting the ODE and using an IMEX like you suggested. Then I will work on compiling OpenBLAS as suggested by @ImreSamu with a build optimized for AlderLake and AVX2 and we’ll see what happens.

This is fun and so educational!

EDIT: It is worth noting that the optimized code at this stage runs on the M1 Mac in 700 ms, a speedup of 16%. I should definitely be able to squeeze more performance out of this.

---

_[View the full topic](https://discourse.julialang.org/t/multithreaded-code-on-beefy-computer-runs-just-as-fast-as-serial-code-on-m1-mac/75210)._