DirectXMath VS glibc

For those unfamiliar, like I was, DXM is DirectXMath.

Alongside MiniEngine, you’ll want to look into the DirectX Toolkit. This is a set of utilities by Microsoft that simplify graphics and game development. It contains libraries like DirectXMesh for parsing and optimizing meshes for DX12, or DirectXMath which handles 3D math operations like the OpenGL library glm. It also has utilities for gamepad input or sprite fonts. You can see a list of the headers here to get an idea of the features. You’ll definitely want to include this in your project if you don’t want to think about a lot of these solved problems (and don’t have to worry about cross-platform support).

Bad news. For SIMD there are not cross-platform intrinsics. Intel intrinsics map directly to SSE/AVX instructions and ARM intrinsics map directly to NEON instructions.

For cross-platform, your best bet is probably https://github.com/VcDevel/std-simd

There's https://eigen.tuxfamily.org/index.php?title=Main_Page But, it's tremendously complicated for anything other than large-scale linear algebra.

And, there's https://github.com/microsoft/DirectXMath But, it has obvious biases :P

I am not in gamedev, but work with 3D graphics, we use DirectX 11, so DirectXMath was a natural choice, it is header only, it supports SIMD instructions (SSE, AVX, NEON etc.), it can even be used on Linux (has no dependence on Windows). It of course just one choice: https://github.com/Microsoft/DirectXMath.

I never really used GLM, but Eigen was substantially slower than DirectXMath https://github.com/microsoft/DirectXMath for these things. Despite the name, 99% of that library is OS agnostic, only a few small pieces (like projection matrix formula) are specific to Direct3D. When enabled with corresponding macros, inline functions from that library normally compile into pretty efficient manually vectorized SSE, AVX or NEON code.

The only major issue, DirectXMath doesn’t support FP64 precision.

If you’re the author, consider comparisons with the industry standards, glm and DirectXMath, which both ensure easy interoperability with the two graphics APIs.

Good article, but note that if the hardware supports the division instruction, will be much faster than the described workarounds.

Personally, I recently did what’s written in 2 cases: FP32 division on ARMv7, and FP64 division on GPUs who don’t support that instruction.

For ARM CPUs, not only they have FRECPE, they also have FRECPS for the iteration step. An example there: https://github.com/microsoft/DirectXMath/blob/jan2021/Inc/Di...

For GPUs, Microsoft classified FP64 division as “extended double shader instruction” and the support is optional. However, GPUs are guaranteed to support FP32 division. The result of FP32 division provides an awesome starting point for Newton-Raphson refinement in FP64 precision.

For graphics DX math is a very good library.

I wonder how does it compare with Microsoft’s implementation, there: https://github.com/microsoft/DirectXMath/blob/jan2021/Inc/Di...

Based on the code your version is probably much faster. It would be interesting to compare precision still, MS uses 17-degree polynomial there.

I am writing a short paper for school and the topic is Fast Inverse Square Root and alternatives. One of the questions is how the sqrt-function is implemented in glibc. Here is the code of that function.

The code isn't the easiest to read but in glibc it seems that the syscall is only performed if waiter are detected in userspace during an unlock operation

https://github.com/lattera/glibc/blob/master/nptl/pthread_mu...

You might still not realize, but floats are a large topic. Have a look at eg. this implementation here: https://github.com/lattera/glibc/blob/master/stdlib/strtod_l.c

This is a nice write up, thank you. However, I'm stilling interpreting this as a "fun trick" rather than the common sense method for solving the problem. For example, looking at the source code for htonl() from glibc: https://github.com/lattera/glibc/blob/master/inet/htonl.c

Second of all: The horrifying truth is that there is no such thing as a canonical text representation for IPv4 (source) (and yes... I am indeed citing the failed attempt to standardize this as my source for it not being standardized). Authoritatively speaking, all possible (non-binary) representations are equally invalid. In fact, text address resolution is typically delegated to the OS kernel, so what constitutes a "usable" address is liable to vary depending on if you're using Linux, OSX, Windows, or Other.

Optimized code gets really weird. The creators of strlen, for example, decided that iterating over each character to find the end was far too slow. So instead, they convert the character pointer into an int pointer and do bitwise manipulation with the int on two different magic numbers so they can check four/eight characters at once.

If you're using a cross-platform C compiler like gcc or clang, you're usually expected to switch to assembly. Here's the syscall instruction in glibc

Maybe you should include the math part of a libc statically with your code. glibc is one option, or dietlibc if you want it to be as small as possible.