# Disable fast-math for a specific function
**URL:** https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160
**Category:** Performance
**Created:** [February 25, 2019, 1:02am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160 "2019-02-25T01:02:20Z")
**Posts on this page:** 7
**Page:** 1
### Author:  [@robsmith11](https://discourse.julialang.org/u/robsmith11)
#### Post date: [February 25, 2019, 1:02am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/1 "2019-02-25T01:02:20Z")
I’ve written a large numeric simulation that benefits substantially from starting Julia with `--math-mode=fast` (79% speed-up). I know this flag can be dangerous, but up until now, everything has worked well.
I’d now like to add Bayesian Optimization to tune a few parameters. Unfortunately, the Gaussian Process MLE breaks when I use explicit parameter bounds with fast-math enabled. Performance is not important for the Gaussian Process – I just need it to work.
Is there a way to disable fast-math for _only_ the call to the Bayesian Optimization function?
I’ve tried switching to explicit `@fastmath` in the obvious parts of my code, but I still get a 43% speed-up by using the global `--math-mode=fast` flag.
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### Author:  [@c42f](https://discourse.julialang.org/u/c42f)
#### Post date: [February 25, 2019, 4:26am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/2 "2019-02-25T04:26:42Z")
I don’t know of a way to do this easily. Looking at codegen (intrinsics.cpp), it seems that codegen just uses the global flags which are set when julia starts. And the `@fastmath` macro is a strictly local transformation.
Perhaps the best you can do here is to start two julia instances; one to do the outer loop with Bayesian optimization (fastmath off) and another (or several) worker processes to run the simulation with fastmath on? This might be a good setup in any case, as with multiple cores/machines you’ll then be able to run several simulations in parallel to feed back into the Bayesian optimization.
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### Author:  [@bennedich](https://discourse.julialang.org/u/bennedich)
#### Post date: [February 25, 2019, 6:33am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/3 "2019-02-25T06:33:36Z")
Explicit `@fastmath` seems like by far the best option to me, if you could achieve about the same performance. I’m curious why you can’t… is it because `--math-mode=fast` applies to code within `Base` and other packages that you can’t reach with `@fastmath`?
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### Author:  [@robsmith11](https://discourse.julialang.org/u/robsmith11)
#### Post date: [February 25, 2019, 9:06am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/4 "2019-02-25T09:06:39Z")
I’ll need to profile more carefully to see where the differences are. I haven’t tried `@fastmath` with every function yet, so it’s possible I’ve missed some, or like you said, there’s some code in another package that is affected.
I will be using remote workers, so I may just do that: run a master without fast-math and have all the slaves use fast-math.
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### Author:  [@c42f](https://discourse.julialang.org/u/c42f)
#### Post date: [February 25, 2019, 10:27am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/5 "2019-02-25T10:27:05Z")
The problem with `@fastmath` is that it doesn’t compose so it could be a pretty big burden to add it enough places, especially if using external libraries.
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### Author:  [@kristoffer.carlsson](https://discourse.julialang.org/u/kristoffer.carlsson)
#### Post date: [February 25, 2019, 10:30am UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/6 "2019-02-25T10:30:46Z")
Would be interesting to profile with and without fastmath and see if there is something specific that sticks out.
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### Author:  [@StefanKarpinski](https://discourse.julialang.org/u/StefanKarpinski)
#### Post date: [February 25, 2019, 4:05pm UTC](https://discourse.julialang.org/t/disable-fast-math-for-a-specific-function/21160/7 "2019-02-25T16:05:45Z")
Almost all optimizations that fastmath allows can be done manually by some combination of:
1. explicit algebraic simplification of expressions,
2. `@simd` annotations to allow floating-point re-association across loop iterations,
3. use of `muladd` to compute `a*b + c` operations in a single operation.
It’s definitely some work but if you can identify which functions are sped up by `--math-mode=fast` then you can speed them up manually. It’s probably just a handful of functions that are making most of the difference. My guess is that there’s a few loops that need `@simd` annotations in order to vectorize.