DirectXMath VS OpenBLAS

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.

The Fortran implementation is just a reference implementation. The goal of reference BLAS [0] is to provide relatively simple and easy to understand implementations which demonstrate the interface and are intended to give correct results to test against. Perhaps an exceptional Fortran compiler which doesn't yet exist could generate code which rivals hand (or automatically) tuned optimized BLAS libraries like OpenBLAS [1], MKL [2], ATLAS [3], and those based on BLIS [4], but in practice this is not observed.

Justine observed that the threading model for LLaMA makes it impractical to integrate one of these optimized BLAS libraries, so she wrote her own hand-tuned implementations following the same principles they use.

[0] https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...

[1] https://github.com/OpenMathLib/OpenBLAS

[2] https://www.intel.com/content/www/us/en/developer/tools/onea...

[3] https://en.wikipedia.org/wiki/Automatically_Tuned_Linear_Alg...

[4]https://en.wikipedia.org/wiki/BLIS_(software)

Yeah. I'm going to be helping to work on expanding CI for OpenBlas and have been diving into this stuff lately. See the discussion in this closed OpenBlas issue gh-1968 [0] for instance. OpenBlas's Skylake kernels do rely on intrinsics [1] for compilers that support them, but there's a wide range of architectures to support, and when hand-tuned assembly kernels work better, that's what are used. For example, [2].

[0] https://github.com/xianyi/OpenBLAS/issues/1968

[1] https://github.com/xianyi/OpenBLAS/blob/develop/kernel/x86_6...

[2] https://github.com/xianyi/OpenBLAS/blob/23693f09a26ffd8b60eb...

We'll have to wait until part 2 to see what they are actually proposing, but they are trying to solve a real problem. To get a sense of things check out the handwritten assembly kernels in OpenBlas [0]. Note the level of granularity. There are micro-optimized implementations for specific chipsets.

If progress in ML will be aided by a proliferation of hyper-specialized hardware, then there really is a scalability issue around developing optimized matmul routines for each specialized chip. To be able to develop a custom ASIC for a particular application and then easily generate the necessary matrix libraries without having to write hand-crafted assembly for each specific case seems like it could be very powerful.

[0] https://github.com/xianyi/OpenBLAS/tree/develop/kernel

libraries mkl_rt not found in ['C:\python\lib', 'C:\', 'C:\python\libs'] ``` Install this and try again. Might need to reboot, never know with Windows https://www.openblas.net/

There isn't any fortran code in the repo there itself but numpy itself can be linked with several numeric libraries. If you look through the wheels for numpy available on pypi, all the latest ones are packaged with OpenBLAS which uses Fortran quite a bit: https://github.com/xianyi/OpenBLAS

Sure - write functions in another language, export C bindings, and then call those functions from Python. An example is NumPy - a lot of its linear algebra functions are implemented in C and Fortran.

Read the official docs yet?