Internal usage of f64 in some f32 functions

[korken89]

Hi, I had a look over the library and something struck me as odd.

For example in sinf and k_sinf the input arguments are being cast up into f64, which will incur a significant run-time cost on no_std systems which only has an single precision floating point unit.
Moreover, the calculation being done in, for example here:
https://github.com/japaric/libm/blob/3932e2ea8e7e8f782cea6b243c3032d415b8afeb/src/math/sinf.rs#L62
The f64 is only used for sums, which does not need the extra precision given by f64.

This looks like a copy/paste bug to me, but I would like to hear your reasoning about this.

[korken89]

I saw that the kernel in k_sinf utilizes f64 and that high powers (x^5) are being used as well.
Is this what causes the use of f64? Has the precision loss vs f32 been benchmarked?

[korken89]

I did a quick benchmark of the kernel over the [0, PI/4] interval (100k steps) utilizing this test code:
https://gist.github.com/korken89/fd6e2fa5f4ededfdd37cd5e846c7819d

The maximum error is 0.000000059604645.
A quick test seems to indicate that the use of f64 is not needed.

[korken89]

I extended it for cos as well and added relative error (10M steps): https://gist.github.com/korken89/6a219e0cf87be244009086072816b524

Max error sin = 0.000000059604645
Max error cos = 0.00000011920929
Max relative error sin = 0.00000011920926
Max relative error cos = 0.000000168585

[burrbull]

These implementations from musl library.
We could add other with cargo feature to select accuracy or performance.

[korken89]

What I am trying to point out is that there is no difference when the max error is 0.000000168585, it is at the edge of precision for f32 with approx. 7 decimals of precision.
For the developer that wants precision, they should be using f64 to start with. :)

[japaric]

copy pasting some of my IRC comments here for posteriority / visibility:

• the crate (this crate) is a 1:1 port of MUSL libm
• yes some f32 functions (internally) use f64 for some reason
• we can change the implementations to only use f32 but (a) the original code must be compatible with MIT / Apache 2.0 and (b) it needs to be thoroughly tested against the C implementation

[vks]

I would expect it is to avoid cancellation errors.

[korken89]

From the internal kernel of the sinf/cosf, it is simple 5th order polynomial approximations over the minimal range needed to recreate any sin/cos value.
The only one that is not clear for me yet is the pi/2 modulo operation, which goes through a lot of work. I'm looking into it as well.

[porglezomp]

@korken89 could your run your test again measuring the max error in ULPs or percent error instead of in absolute? Absolute error seems like it could be misleading here when the results are potentially small.

[korken89]

Please see my previous response, take the relative error *100 and you have percent. And is on the order of 0.000017% worst case. :)

[porglezomp]

Ah, my bad, I misread what you were doing, thank you.

[porglezomp]

I slightly modified your test bench to get the error in ULPs (maybe someone check my work…): https://gist.github.com/porglezomp/3ec824f07d15ba5383992f573e6be120

The max error in sinf is 1 ULP, and the max error in cosf is 2 ULPs.

[korken89]

Thank you for adding! ULPs really shows how close the kernels are writhin it's designed bounds.
I just have to give the pi/2 modulo function a look, it's the only thing in the way of a full f32 implementation.

[korken89]

I found this look into sine approximation (in Rust) utilizing Chebychev polynomials, but over the [-pi, pi] domain.
Quite interesting and a good read: http://mooooo.ooo/chebyshev-sine-approximation/

However, this is not the current issue, but still a good read.

[japaric]

I looked at /usr/arm-none-eabi/lib/thumb/v7e-m+fp/hard/libm.a and, for example, asinf doesn't seem to be using any f64 code. This file is part of the Arch Linux arm-none-eabi-newlib package.

We could use the newlib implementations of some f32 functions but we would have to tweak the test system to generate the expected outputs using newlib.